Underwater 3D Data Collection

1.0 Introduction:

It seems inappropriate to call this world “Earth” when much of its surface is made up of liquid, not just any liquid, but water – the most precious fluid for life.

According to estimates from the World Shipping Council, on average over the past nine years over 1,582 cargo containers have capsized at sea annually. Yet these lost containers only represent a fraction of man-made debris accumulating in our oceans. The UNESCO projected in 2007 that there were already over three million containers sitting unseen on ocean floors. Meanwhile, experts say some $60 billion worth of treasures could be lying in the millions of shipwrecks on the seafloor. However, discarded fishing gear such as nets, lines, and traps make up the bulk of plastic pollution currently threatening marine life – with estimates of more than 64,000 tons dumped into oceans annually. A study of 42,000 tons of plastic waste found that 86% was abandoned fishing equipment.

In contrast to items lost on land which can often be retrieved, the immense majority of our seas remain unexplored, unobserved, and unknown. According to NOAA’s 2018 findings, a massive 80% of the ocean has yet to be directly experienced by humans.

Various initiatives have sought to address and mitigate the scale of losses at sea. Efforts range from environmental work to employing novel technologies aimed at better imaging underwater to facilitate targeted recovery missions. Notably in 2019, conservationists removed 40 tons of derelict fishing nets from the Pacific over 25 days.

NOAA research reveals just 35% of US coastal waters have been thoroughly mapped using current methods. With underwater robotic exploration remaining costly, alternative remote techniques like sonar are predominantly used but have still only imaged 9% of ocean floors. However, burgeoning companies are now harnessing sonar and drones in hopes of fully mapping the seabed by 2030 – having already expanded coverage from 6% to 15% over the past two years.

Contemporary technical solutions provide the most pragmatic path toward precisely locating sunken debris and better understanding our submarine domain in a timely and cost-effective manner. Major advances have been achieved recently in 3D seafloor data collection capabilities vital to satisfying this critical informational need.

1.1 3D Data Collection and Object Detection:

Several key technologies exist that can remotely image underwater objects by emitting and receiving waves. Radar, lidar, and sonar all operate on the core principle of transmitting a wave and gauging the delay of any reflections. Specifically, radar uses radio waves, lidar employs light waves, and sonar harnesses sound waves.

However, the applicability of these approaches varies greatly depending on environmental factors. Due to water’s unique properties, radar, lidar and sonar each have advantages and limitations for underwater use. For instance, radar waves don’t propagate well in water. Meanwhile, lidar’s light waves don’t travel far below the surface. In contrast, sonar leverages sound waves, which disseminate exceedingly well in water, rendering it the premier echo-based solution for underwater contexts.

Essentially, all depend on reflection timing for remote detection. But the surrounding medium strongly dictates which wave type – radio, light or sound – enables the longest effective range and most precise details. Therefore, carefully considering each method’s suitability for the aquatic setting is prudent when selecting an imaging technology.

1.1.1 Radar:

The origins of radar can be traced to experiments in the late 1800s by Heinrich Hertz, which showed that radio waves could reflect off metal objects. However, it was not until the early 1900s that radar found practical applications. One early use was Christian Hulsmeyer’s ship detection system in foggy conditions using radio waves to aid navigation. Subsequently, various European nations and the U.S. significantly advanced radar technologies during World War II for aircraft detection.

The term “RADAR” was officially coined by the U.S. Navy in 1940, standing for Radio Detection And Ranging.

At its core, a radar system uses the transmission and reception of radio waves to determine distance, location, and speed of objects. It includes a transmitter that generates electromagnetic signals across radio and microwave frequencies, along with a receiver to detect returning waves reflected off targets. By measuring the time, velocity, and angle of returning waves, properties of detected objects can be derived.

Initial applications focused on military uses such as locating ground, air, and sea targets. As the technology matured, civilian usages emerged – for example, aviation radar enables low-visibility landings. Weather radar tracks precipitation. Maritime radar aids navigation. Benefits include functioning irrespective of weather through suitable wavelengths, enabling 3D imaging and rapid data collection about a target’s position and distance. However, signals can be blocked during transit by some materials. It also cannot detect deeply submerged, colorless, or nearby objects as effectively as other technologies at times.

1.1.2 Lidar:

The laser was first invented in 1957, laying the groundwork for future lidar applications. In the 1960s, the National Center for Atmospheric Research began pioneering the use of lasers in combination with radar to measure cloud sizes – representing some of the earliest attempts to use laser technology for distance measurement and object characterization. Lidar then gained broader recognition following its deployment on the Apollo 15 mission to map the lunar surface in the 1970s. Throughout that decade, NASA and other organizations increasingly leveraged lidar for applications like topographic surveying due to its ability to rapidly collect high-resolution geospatial data. Rapid technological progress continued through the 1990s as lidar scanning capabilities advanced. Today, lidar remains an area of active research and development focused on enhancing measurement accuracy and point density. Notable ongoing efforts include the USGS-partnered 3D Elevation Program to generate a comprehensive national topographic dataset for the United States using airborne lidar. Fundamentally, lidar functions by emitting pulsed laser beams and calculating the time of flight between transmission and reception to precisely determine target distances. Thousands of these time-of-flight measurements can be made per second, ultimately producing a point cloud detailing x, y, and z coordinates across a surface or scene.

Traditionally, lidar has found widespread application in urban planning, mapping terrain features, and monitoring geological changes over extended periods. Recent lidar integration into autonomous vehicles leverages its object detection prowess to help navigate complex roadway environments safely. While lidar delivers highly accurate geo-referenced models due to dense point sampling, system costs remain prohibitive for some applications. Data processing overhead can also be significant. Underwater usage introduces additional noise-related challenges as the laser signal speed decreases in water.

1.1.3 Sonar:

While bats have utilized echolocation for millennia, it was not until 1916 that humans first replicated this ability through engineering. Paul Langevin developed the initial functioning sonar prototype. Though crude and energy-intensive, he and Constantin Chilowsky improved it within two years to detect metal from 200 meters. Passive sonar detection of nearby submarines during World War I represented some of the earliest practical applications. Later, Germans published the first academic paper on sonar effects during the war, but it received little attention. World War II spurred further innovations, culminating in effective active sonar critical to anti-submarine warfare. Post-war, sonar advancements slowed until recent decades. The underlying process involves transmitting sound waves and calculating object distances from the return reception time. Multiple measurements are compiled into visual representations. Marine navigation commonly employs sonar for locating underwater obstacles, resources, and wildlife. Its efficiency in water stems from sound propagating well through solids and liquids. Beyond marine contexts, sonar also enables medical ultrasound scanning. While low-cost to implement, sonar is limited by noise interference and signal dispersion over longer distances. Lower frequencies partly address this, with reduced resolution. Wildlife presence similarly impacts performance if occupying overlapping sound spectra. Overall, sonar remains a practical choice for many aquatic or submerged detection applications due to its fundamental workings in transmitting and receiving audible signals.

2.0   Background:

This section focuses on the 3D data collection method selected for this project and analyzes previous sonar experiments to better understand sonar device operation. Maximizing device efficiency is then explored, concluding with a summary of how sonar can help solve the problem of locating marine debris. Specifically, the 3D data collection technique that will be implemented is discussed. Prior sonar tests are evaluated to gain insight into sonar functionality. This includes examinations of factors like resolution, range, profiling capabilities, and data output formats. Additionally, methods for optimizing sonar performance are outlined. Considerations cover aspects such as transducer selection, beam angles, sweep patterns, and appropriate frequencies for the target environment.

In closing, an overview is provided on how the chosen sonar-based approach supports addressing the overarching goal of precisely situating discarded materials underwater. The technology evaluation and optimization strategies discussed pave the way for effective marine litter detection.

Background:

2.1   Chosen Method – Sonar:

Fundamentally, the overarching issue relates to the massive volume of objects lost at sea annually. While containerized cargo contributes greatly to marine debris amounts, regularly misplaced watercraft items supplement the overall total as well. Developing a system to pinpoint such sunken materials could aid recovery from ocean floors. Considering the initial problem presentation coupled with the advantages and limitations of different 3D data collection techniques previously reviewed, sonar emerges as the most suitable approach for identifying and visually representing marine litter and flotsam underwater.

However, numerous sonar scanning technology characteristics require examination to define the engineering specifications for the final solution. Each attribute incorporates trade-offs impacting the performance capabilities of the system ultimately designed. Specifically, variables like frequency selection, beam widths, array configurations, and output formatting must be optimized relative to the target deployment environment and intended data applications. A full accounting of resolution, range, coverage needs, and other factor interdependencies will lead to designing a sonar system tailor-fitted to solve the stated marine debris localization challenge.

2.2   Understanding Sonar Functionality:

This section goes over the various forms of sonar and the advantages and disadvantages of those systems.

2.2.1 Types of Sonar:

There are mainly two kinds of sonar systems: passive and active. Passive sonar is composed only of a hydrophone, which is also referred to as a microphone. Active sonar systems consist of a transducer and a hydrophone according to Waite (2002).

Passive sonar is employed to pick up sounds, or noise, created underwater. This sound comes from different sources such as living organisms and other vehicles. Passive sonar systems do not emit any signals as they only use a hydrophone. Because of this, it is frequently utilized in military settings when submarines do not want to create their own sound (NOAA, 2018). Furthermore, passive sonar does not provide data on the range of the target it is detecting. Instead, it makes a broad statement that an object is nearby the sensor. Nonetheless, employing passive sonars in conjunction with other passive sonars could enable the calculation of a target’s location via triangulation (Waite, 2002). The formula for determining the signal-to-noise ratio (SNR) in passive sonar is SNR (decibels) = source level – transmission loss – (noise level – ambient noise). SL in this equation represents the source level of the signal sent by the sonar. TL stands for transmission loss, measured in decibels, indicating the amount of signal intensity lost when detecting objects at a greater distance. TS is the target’s strength, which decreases as the distance from the sonar increases. The noise level in the water is labeled as NL, while AG represents the array gain (Discovery of Sound in the Sea, 2002).

Active sonar produces a sound pulse via the transducer. When the sound waves pulsate towards a target, the target will bounce the waves back. In the end, the hydrophone picks up these waves. cases, the outcome may be unpredictable.

2.2.2 Sonar Targets:

Multiple tests with different targets moving at varying speeds and locations to sonar are necessary to effectively use its capabilities. Targets may be any physical object placed within the operating device’s line of sight. Three targets, including a decommissioned lobster trap, a 25 pound anchor, and an enclosed cross made of PVC piping, were used for testing in this scenario.

2.2.3 Side Imaging and Down Imaging Sonars:

Data from numerous tests was analyzed to enhance the comprehension of output data generated by commercially accessible sonar devices. The primary aim was to grasp the distinction between down imaging and side imaging in a practical environment. Different recordings that had been gathered earlier were reviewed. The recordings showed the anchor target being moved in various ways in front of the sonar devices during the recordings. The sonars utilized and the experiments conducted are listed in detail below.

The initial recording documented the lowering and lifting of an anchor to the ocean floor. This footage was obtained using down imaging with a Garmin 53CV chartplotter (Michalson 2019).

The following video shows the same anchor being lowered, with pauses at different depths in the water column. This process was done repeatedly as the anchor approached the ocean floor, and then it was repeated on the way back up. Down imaging was also utilized on a Garmin 53CV chartplotter for this recording.

These tests assess how effectively sonar devices utilize down imaging functionality. Each test has a clearly defined path of motion that aligns with the expected description of the test. As previously stated, down imaging uses sound pulses to capture objects below the sonar device, sending them towards the object at a right angle to the water body floor. The recordings above verify this by displaying just one perspective of the moving target.

Afterward, the recording was reviewed to gain a deeper insight into the characteristics related to side imaging sonar equipment. Once more, an anchor was dropped to the ocean floor and then brought back up. This recording utilized the side imaging function on a Garmin 73SV chartplotter.

The trajectory of the anchor is once again evident in this footage. Nevertheless, the main distinction between side imaging and down imaging is understood. The illustration shows that the transducer releases various pulses in side imaging to detect objects sticking out on either side, running parallel to the bottom of the water. As a result, the movement of the anchor is visible on both sides of the footage, with the transducers path depicted by the yellow line.

Both side imaging and down imaging are crucial because they capture the movement of the target in the recorded footage. Furthermore, both of these imaging methods only show targets in two dimensions. The anchor’s movement in all tests is depicted as a line due to the combination of these features, instead of a 3D image.

2.2.4 Three Dimensional Imaging Sonars:

The recordings focused on using the anchor as a target to observe how object motion is captured by two-dimensional sonars. Nevertheless, certain applications necessitate the locating of the target through a three-dimensional image. Three-dimensional imaging sonars are frequently utilized in these applications. To gain a deeper understanding of the distinction between these sonar variations, additional recordings were examined utilizing sonar devices with three-dimensional imaging capabilities. These recordings involved using a lobster trap and PVC cross as the other two targets. Although the recordings above captured a target in motion, the three dimensional sonar recordings used targets that were stationary. The recordings were created with a Sound Metrics Aris Explorer 3000 (Pulsone, 2019).

This sonar type captures a more comprehensive three-dimensional image of the target. The lobster trap is shown in the image on the left, while the PVC cross is shown in the image on the right. The target’s three-dimensional shape is easier to see in both recordings compared to side imaging and down imaging. Furthermore, the measurements displayed on the side indicate the sonar’s range in the direction it is facing, not just the depth of the water. When the objects are in the viewing range of a 3D imaging sonar, the trajectory of the object is not recorded. Instead, the object is observed moving in a manner akin to how it would move in a video recorded on land using a camera.

In general, these recordings showcase different kinds of sonar devices that are currently available in the market. It is important to understand the varying functionality and data output of these devices to determine the optimal approach for designing the original sonar. Each kind has different pros and cons, so choosing the right sonar depends on how it will be used.

2.2.5 Operating Environment:

A wide range of environments can be encountered when collecting data underwater. Aquatic areas can vary from being shallow and transparent to deep and cloudy. Furthermore, the operational setting may be a regulated space, like an indoor pool, or a natural body of water like a lake, river, or ocean. Every one of these regions impacts the manner in which sonar information is obtained and managed.

Normally, sonar devices have an operating range of 10 to 100 kilometers (Smith, 2003). Nevertheless, because of the specific focus of this object recognition task, only underwater distances of no more than a few hundred meters will be taken into account, as the sonar will be gathering data on the seafloor directly beneath it. Sonar works more effectively at shallow depths, providing clearer images compared to deeper levels. This occurs because the sound waves bouncing off the ocean floor near the transducer will have higher intensities and more quantity since they are not lost from reflections and cannot return to the source of transmission. Moreover, a greater number of beams will be reflected back to the object when the distance traveled is shorter, as the beams will encounter the object with a higher concentration (NOAA, 2013).

As previously stated, the quality of scanned data is impacted by the clarity of the water source. Objects are more effectively detected in clear water due to less interference from suspended particles, allowing sound waves to echo more clearly. Additionally, items submerged in deeper water are unable to reflect bright light effectively, leading to a less detailed image of the object (NOAA, 2013). Nevertheless, this is not a significant concern since sound waves are 2,000 times longer than light waves, providing ample space for the waves.

2.2.6 Data Collection Rate:

Considering the speed of collecting 3D data underwater is crucial as it can impact the accurate identification of man-made structures beneath the water’s surface. One important aspect is the speed at which the sensor moves, especially since it will probably be fastened to a boat in the ocean. The resolution of the image observed when using fish finders, which use sonar technology to locate fish or underwater structures, is affected by the speed of the boat. The faster the boat moves, the lower the image quality will be. This is because the sonar device will release sound waves at a consistent rate while traveling a certain distance, regardless of its speed. Consequently, an increase in speed will disperse the locations where sound waves bounce back to the receiver, leading to a decrease in image quality (Martens, 2017). The resolution of the image is also affected by the depth at which the speed is traveled. Higher speeds will not compromise image quality in shallower regions because the sound waves take less time to travel to the target and back to the sonar. The ideal speed at which the sonar should be moved during scanning depends on the depth of the area being scanned to obtain a high-quality image for the project at hand (Martens, 2017).

2.2.7 Data Resolution:

The beam size used in a scan will impact the level of detail or resolution of the image captured from underwater objects and structures. As previously stated, sonar functions by sending out a burst of sound waves in order to locate objects. While traveling through space, the waves spread out in the shape of cones, which widen as they go further. Numerous sonars allow users to adjust the scanning frequency, thereby influencing the width of the sound wave cone. A broader beam is ideal for swiftly scanning a shallow area. As the cone widens, the water deepens. At some point, the sound waves will be so widely dispersed that reflections will not be picked up and objects at that particular distance will go undetected. Thin beams are better at detecting objects at increased depths because the cone does not widen as much, resulting in clearer reflections. Lower frequencies lead to wider beams, while higher frequencies lead to narrower beams.

2.2.8 Data Visualization:

Common techniques for visualizing sonar data include side imaging and down imaging. Every technique creates a unique image with its own set of pros and cons based on how it will be used.

The most common use of side imaging sonar is to observe the bottom of a body of water. It depends on sending out sound waves in a horizontal direction underneath the water’s surface. While going through this procedure, objects sticking out of the floor will bounce the waves back to the sonar device. Side-imaging sonar is often employed to locate wreckage, like shipwrecks, by considering the emission geometry (Enviroscan, 2020).

The diagrams above demonstrate side-imaging sonar and a fish finder displaying data. In the image generated, the objects are shown alongside the sonar, running parallel to the bottom of the body of water.

Down imaging sonar sends sound waves vertically from the device’s base. The image that is sent back shows the layout of the floor beneath the sonar tool and is at a right angle to the sonar beams. Items in the picture are also shown. As the sonar moves through the water, anything directly underneath reflects the signal and appears on the device screen (Humminbird, 2019).

The above photos display an example of down imaging data visualization as well as a diagram of sound wave emission from the sonar.

2.2.9 Power Consumption:

The sonar equation estimates the signal-to-noise ratios (SNR) for sonar scanning systems. The signal-to-noise ratio (SNR) is the primary factor that influences the receiver’s ability to distinguish the reflected signal from background noise in a body of water in a sonar system. The SNR considers the signal’s source level, signal propagation in water, signal absorption in the environment, reflection losses, ambient noise, and the sonar receiver’s characteristics (Urick, 1983).

By using the sonar equation and SNR, it is possible to calculate the necessary transmit power considering all factors influencing the signal during transmission, reflection off the target, and reception back at the sonar source. The intensity will decrease as the range increases for a constant total power output according to the Federation of American Scientists (1998).

2.2.10 Mechanical Attributes:

Before connecting a sonar device to a moving object, it is important to consider the mechanical aspects of the system. Typical sonar attachment techniques include towing and using mechanical fixtures. Sonar systems that are towed behind a watercraft are typically used for protection. The wiring system known as ACTAS is often released off the back, attaching them to the vessel. In addition, sonar devices like fish finders are installed on boats for their usage. This is typically accomplished using screws or brackets that come with the fish finder. Next, the transducer is installed beneath the surface of the water at the boat’s perimeter. It is crucial to take into account the physical characteristics of the device that must be balanced when it is physically connected. For instance, the bracket or bolt set must be sturdy enough to avoid device damage due to water resistance and the weight of the sonar while in motion.

Furthermore, the transducer must be securely attached to the boat to withstand the forces it experiences while moving in the water. The transducer must possess fluid dynamic properties to minimize the impact of water resistance on the system.

2.2.11 Potential Hazards:

Multiple dangers can occur when using electronics around water. The water poses a significant threat to the electronics inside the sonar, requiring the board to be equipped with waterproof protection. If the waterproofing were to fail, nearby animals could also be put in danger by these electronics.

If specific frequencies are utilized, there is a potential danger as well. Certain frequencies can be detrimental to marine organisms, causing harm and potentially resulting in fatalities. The Navy’s initial sonar system produced a sound level of 235 dB, whereas the loudest rock band in the world only reached 130 dB. These high levels of noise also pose a significant danger as they can easily start to cause harm (Scientific American, 2009). The Navy has acknowledged that its experiments have resulted in more than 3 million marine mammals being harmed and 2 million individuals experiencing temporary hearing impairment (Naval Facilities Engineering Command, 2018).

2.3 Derived Engineering Requirements:

This section will cover the ultimate engineering needs of the system, along with a basic block diagram to aid in the reader’s understanding of the final product. A decision will be made for each attribute of the sonar mentioned earlier to determine which characteristic to prioritize in building a new sonar device for resolving the initial problem. Since passive sonar cannot accurately determine an object’s distance or location, an alternative active sonar method will be utilized to locate marine litter and debris. Active sonar is more effective at finding objects underwater because it can emit sound pulses and measure the time it takes for them to return. In terms of the operating environment, a controlled and transparent aquatic area with different depths, like a pool, will be utilized for efficient data collection. A regulated setting enables concentration on sonar ideas like efficient data presentation of known-sized and located targets, without the need to account for excessive noise in murky water environments. In this manner, findings can be drawn about the act of finding items and applying these findings to real water sources. The rate and resolution of data collection will be primarily determined by the specific sonar device being considered. Furthermore, after gathering sample data and examining the average size of the data, a determination will be made regarding the required capacity of onboard data storage. Once these choices have been finalized, a monitor will be chosen that can effectively display data at the desired quality.

In most cases, the constructed vessel will include a platform containing a buoyant base like styrofoam enclosed in a wooden box. Ideally, the sonar device attached to the platform will look like a typical fish finder. Hence, the gadget

2.3.1 Block Diagram:

The diagram shown below illustrates a schematic of the overall system design intended to gather information using sonar technology to detect and display underwater objects in three dimensions.

The process starts with a universal converter which will transmit and receive the sound waves for detecting objects under the water. The initial sonar recordings will be analyzed with algorithms to create an image from the data collected during the experiment. After creating an image, additional image processing techniques will be used to recognize object shapes and then notify the user when these shapes are detected.

3.0   Determining System Requirements

This section explores different factors that impact the overall system design, ultimately resulting in the technical requirements needed for system functionality.

3.1   Wave Properties:

The characteristics of the wave sent out by a sonar play a crucial role in determining what the sonar is able to detect. Resolution and distance are key factors to think about when making trade-offs. Higher frequencies provide greater resolution allowing for the observation of smaller objects, while lower frequencies penetrate deeper into the water with minimal distortion. To effectively find marine litter like lobster traps, the sonar needs a high enough resolution. According to wave physics, in order for a wave to find an object, the wavelength must be shorter than the object’s smallest side. The majority of lobster traps on the market come with sturdy 1″ by 1″ mesh sides or 25.4mm by 25.4mm. Adding to this is the fact that sound travels at a speed of 1500 meters per second in water (Nieukirk), the equation for wave speed is calculated to find the required minimum frequency.

• ? = ??
• ? = ?/?
• ?  =  1500/.00254  = 59???
• ?_????? > 59???

The next factor to take into account is the highest frequency. Determining the highest frequency is not as simple. As the absorption of the energy increases, so does the frequency. In order for the wave to travel to the destination and return, the initial energy of the wave needs to be increased. Lobster traps are typically located at depths of around 120 fathoms (McCarron) or roughly 220 meters. Organic life can experience immediate damage to hearing or organs at sound levels exceeding 120 decibels, according to the CDC. Garmin, a popular sonar manufacturer, reduces their signal loss to around 25 decibels when at the maximum range. Taking into account both of these factors, the highest possible decibel starting point is around 135 decibels. Additionally, Garmin indicates that their sensors have a trigger threshold of about 110 decibels. With a current max depth limit of 220 meters for lobster traps, it is recommended to increase the maximum depth to 250 meters. A distance of 500 meters must be traveled at this increased depth. The highest frequency that can be utilized is approximately 200kHz, with a maximum loss of 25 decibels.

To prevent interference, the previous signal must have had enough time to travel the entire distance and return. Our sensor can transmit up to a round trip distance of 500 meters. A pulse can be sent every 1/3 second in water without interference because sound travels at a speed of 1500 meters per second. To sum up, the wave should fall within the range of 7 to 200 kHz in terms of frequency, pulse at a rate of 3Hz or less, and maintain a return sound pressure level of around 110 decibels.

3.2  Transducer, Transmitter, and Reducer:

The positioning and configuration of transducers play a crucial role in sonar system setup. Typically, a linear configuration of transducers is necessary because sonar operates in multiple directions. The information is found in the Garmin spreadsheets. Guide for selecting transducers. Most transducers typically have a trigger threshold of around 110 decibels.

Finally, while increasing the number of transducers improves sensor accuracy, having a minimum of two transducers is essential for accurately mapping an area, as one transducer alone cannot create a proper map.

3.3 Data Processing Capabilities:

After the transducer can transmit and receive sound pulses, the following stage in developing a working object detection system involves analyzing the signals that bounce back from the target. Once the scan data is gathered, it will be saved onto a memory device of the right capacity on board. This will be elaborated on in more depth in the following section. The optimal location for data storage and processing will be on the device itself. This action is necessary to give the user a quick response and notify them once the desired object is found. In the past, it was more advantageous to off-load large amounts of data processing due to computing power limitations, but now with advanced technologies, all computations can be done on a portable computer.

3.4   Data Storage:

During the sonar device operation, the scans will be recorded to help visualize and process the collected data. To preserve the scanned information, it needs to be kept inside the sonar device that was created. Multiple tests were conducted with different Garmin fishfinders to calculate the necessary data storage capacity for the device. In-depth descriptions of these tests are available in the Background section of this report. Based on the results of these tests, it was found that the collected data varied in size, ranging from a 4 MB file for a two-minute recording to a 20 MB file for a four-minute recording. Furthermore, according to Garmin’s website, approximately 200 MB of space is used by 15 15-minute recordings (Garmin). To calculate the estimated data required for one minute of recording, the ratio for each example was established and then the average MB per minute was computed.

4 ?? ÷ 2 ??? = 2 ??/???

20 ?? ÷ 4 ??? = 5 ??/???

200 ?? ÷ 15??? = 13.3 ??/??��

(2 ??/??? + 5 ??/??? + 13.3 ??/???) ÷ 3 = 6.8 ??/???

To simplify, we will round the average to 7 MB per minute for further analysis. Even though we don’t know the exact size and complexity of upcoming recordings, the sonar being developed must have 1GB of storage capacity. According to the analysis provided, approximately 142 one-minute recordings can be stored simultaneously. Furthermore, this will offer considerable versatility in the size and duration of the recordings. The information will be saved on an external hard drive located within the sonar instrument’s processing system. This will make it easy to transfer the recorded data for analysis and processing. In conclusion, the ability to remove a data storage device will allow for deleting recordings when more tests are needed than the 1 GB of space available.

3.5   Imaging Technologies:

To enable the end user to recognize formations in the water, an imaging procedure must be put into place. Imaging sonars work by sending out sound pulses and converting the reflected echoes into digital images (Soundmetrics). In the majority of cases, a sonar image of an underwater object will closely resemble an optical image of the same object.

At present, there are multiple products available in the market that can accomplish this task in a sonar system. These programs are either proprietary, or developed by Sonar Marine electronic companies such as Garmin, Humminbird, and Lowrance. Open source programs like those found in the Python language or MATLAB can be utilized to collect and analyze sonar data to produce a complete scan image. This project will utilize a mix of commercial products and open-source files to collect raw data, sanitize and convert it into depth measurements, display the ocean floor visually, and identify manmade structures on the seabed. Challenges may arise while using the exclusive software of the transducers we select. To address this problem, a breakout box can be employed along with the receiver to identify the signals sent by the transmitter and bouncing back from the seabed to the receiver. A processor with accessible GPIO pins must be paired with an amplification circuit to transmit the ADC readings to the computer executing the mapping software. By utilizing the ADC readings, software must be developed to compute depths and ultimately assemble 2D images to generate a 3D representation of the ocean floor to detect artificial constructions. The Python or MATLAB language can be used to write the ultimate object detection program.

3.6   Localization Technologies:

An important function that this system needs is the capability to pinpoint the location of an intriguing dataset worldwide so the user can revisit it later if desired. A data localization function could also help display all relevant data points on a map of the area to facilitate a comprehensive analysis of the surroundings. To accomplish this goal, the overall system will include a global positioning system (GPS) module. This module enables acquiring NMEA data readings from the National Marine Electronics Association (NMEA). The NMEA created guidelines for connecting marine electronic data to ensure uniformity in the information used by mariners. Every data line from a GPS module includes an NMEA sentence that can be analyzed for longitude, latitude, and altitude, along with date and time details. Utilizing this module along with these communication standards will enable our system to precisely monitor the location of its incoming data points.

3.7   Power:

Almost every fish finder on the market runs on 12-volt batteries. As the sonar system being built will rely on a fish finder, a 12-volt battery will serve as the primary power source. Once the system’s final circuit diagram is completed, the precise energy consumption needed will be determined. Nevertheless, it can be assumed that a 12 volt battery will supply sufficient energy for operating the ultimate system.

The system will be powered by a 12-volt battery with a capacity of 8 amp-hours. The process for determining the stored watt-hours is explained in the following way.

?ℎ ∗ ? = ?ℎ 8?ℎ ∗ 12? = 96 ?ℎ

The power source must be in mobile form to allow the sonar device to be maneuvered around the water body. Using a 12-volt battery will permit this necessity.

3.8   Technical Requirements:

Earlier in this chapter, the essential technical requirements for a sonar device for locating submerged objects were established. Below is a summary of these findings.

• The sonar needs to work with a detachable, external storage device.
• The sonar needs to have the capability to be operated by a 12V battery.
• The sonar transducer needs to have side imaging and down imaging capabilities to effectively analyze data obtained from both perspectives.

A Garmin 73SV Chartplotter display with a CV52HW-TM transducer was chosen based on these specifications. Listed below are a few characteristics of this transducer.

• Conventional measurements of 24 degrees by 16 degrees for the beam
• Connector with 12 pins
• Frequencies of 455kHz or 800kHz for CHIRP, side imaging, and down imaging.
• Compatibility with side imaging and down imaging
• Power transmitted: 500W (RMS) / 4000W (peak to peak)
• Power consumption of 7.1 watts.
• Maximum depth in freshwater is 2300 feet.
• Maximum depth in saltwater can reach up to 1100ft.

Each of these attributes meets the minimum requirements outlined. Additionally, the wide range of provided frequencies allows for further experimentation on data collection and processing.

The only requirement for the sonar display is that it is compatible with the provided transducer. This is why the Garmin 73SV was selected.

4.0   System Overview:

In general, the ultimate system will comprise electrical, software, and mechanical elements. These systems will work together to create a visual representation of the underwater environment they are scanning.

4.1   Proving System Feasibility:

The hypothesis was that if a second transducer was connected to an oscilloscope, it would detect the signals sent and received by the Garmin 73SV chart plotter’s transducer. The following sections provide information on the configuration, implementation, and outcomes of the conducted tests. As previously stated, a Garmin 73SV chartplotter was utilized to conduct the experiments which validated the ability to detect signals transmitted by one transducer using another transducer. The Garmin chart plotter was supplied with a Garmin CV52HW-TM transducer. To ensure accurate testing, an additional Garmin CV52HW-TM transducer was acquired.

Furthermore, a Garmin breakout box and an oscilloscope were also supplied. A breakout box’s purpose is to provide convenient access to wire leads in the transducer without having to open it. Accessing these leads allows for interpreting raw data obtained from the transducer signals. Below you can find an image of the breakout box being used, along with the signal provided by each lead.

The numbers on the breakout box correspond to the following signals generated by the transducer.

1. Depth +
2. Depth –
3. Shield
4. Ground
5. Temperature
6. XID
7. SPD PWR (POSITIVE RECEIVE)
8. Speed (NEGATIVE RECEIVE)

Initially, the breakout box was linked to the primary transducer. Next, the oscilloscope’s ground was linked to the grounding wire of the breakout box, while the oscilloscope probe was connected to the Depth+ lead of the breakout box. Individually, the Garmin 73SV chartplotter was linked to a 12Vdc, 8Ah battery along with the additional transducer.

Then, the whole setup was relocated, and the two sensors were placed in a filled tub. Because we wanted to verify our hypothesis that a second transducer could detect the signals sent and received by the first transducer, we chose to conduct the tests in a controlled environment using a bathtub. Ideally, this would reduce the external noise that hinders signal reception. After being lowered into the water, the transducer connected to the Garmin chartplotter was adjusted to a frequency of 200kHz. Multiple objects were positioned in front of the transducer to verify the accuracy of the recorded information. After confirming this, the oscilloscope was adjusted to measure Voltage Versus Time. Experiments were conducted to calculate the duration between the transmission of the signal by the first transducer on the chartplotter and its reception by the second transducer.

Following the tests, it was verified through the oscilloscope that a transducer could be utilized to measure the time it takes for sonar pulses to travel underwater. The initial graph verifies the capability of our recently obtained transducer in identifying the send and receive signals from our transmitter.

4.2   Electrical System Design:

The electrical system’s goal is to identify sonar crystal feedback and transform it into data the computer can understand. The sonar’s electrical system design consists of two distinct main circuits. One of the components is a circuit that amplifies the signal from the crystal, eliminates any interference, and changes the signal into readable data for the ADC. The ADC is the second part of the circuit. The ADC must convert the analog signal to digital and alert the microcontroller when the signal is ready for reading.

4.2.1 Signal amplification Circuit:

• 4.2.1.1  Frequency and Voltage Limitations:

The crystal functioning as the receiver vibrates at a frequency of 200KHz. During vibration, the crystal generates a voltage ranging from 40 millivolts to 40 microvolts. The signal’s limited number of possible transforms is due to its low voltage and high frequency. The minor voltage is the main challenge in addressing how system noise needs to be managed. Typically, the narrow frequency range would make using a band pass filter the simple solution for noise filtering. The sonar crystal is unable to do this because of the slight loss identified in the passband of the bandpass filter. An instrumentation amplifier was chosen to eliminate signal noise.

The instrumentation amplifier needs a differential input, which can be supplied by the crystal. By subtracting the negative of the differential input from the positive input, the noise from the differential input can be canceled out. The main problem caused by the high frequency is in the operational amplifier. Operational amplifiers are easily affected by changes in frequency. When the frequency becomes too high, the operational amplifier is unable to rapidly switch its output, resulting in an insufficient output.

For best accuracy, the signal output requires the full 0-12V range and with an input at a minimum of 40μVpk, the entire circuit needs a gain of 150000 volts. This is divided into 3 phases with an amplification of 100Av, 10Av, and 100Av. The second gain was reduced to prevent the signals from reaching the output rails of the operational amplifiers as frequently. The circuit must have a minimum gain bandwidth product of 20MHz. The circuit is anticipated to smoothly oscillate between 0 and 12 volts. A minimum slew rate of 15V/μS is necessary for this.

4.2.1.2  Instrumentation Amplifier:

The instrumentation amplifier is divided into two stages. The initial phase is the booster stage. This booster stage is crucial as it elevates the signal’s voltage beyond the threshold at which noise can greatly affect it. There is only one power source in the entire circuit. The problem arises when operational amplifiers are unable to amplify a signal negatively unless a dual power supply configuration is used. Providing a voltage bias to the operational amplifier can resolve this issue. To maximize signal amplification, a voltage bias set at 6V (half of Vdd) is utilized.

This does result in two minor problems. One concern is that the crystal is vulnerable to prolonged high voltage. To protect the crystal, a capacitor was used to disconnect it from the operational amplifier. Another problem is that the operational amplifier may lose the signal because of impedance caused by the voltage bias. To prevent the voltage bias from affecting the signal, a minimum of 3MΩ should be used in parallel with the operational amplifier’s internal resistance of 300KΩ. In any situation where a serial connection is employed, it is recommended to use a maximum 3KΩ resistor, although a 1KΩ resistor was often utilized as well.

Following the boost stage, the signal moves on to a stage that boosts and differentiates it. As both signals have been amplified equally so far, their noise levels remain the same and are cancelled out at this step. This stage also receives a voltage bias to address the single supply issue mentioned before.

4.2.1.3  Boosting stage:

A single inverting amplifier is utilized in the last boosting stage. A capacitor is used to separate the output from the previous stage, allowing for voltage biasing of the signal. The result of this phase produces a signal at half Vdd with a 0-Vdd bias.

4.2.1.4 Power:

The circuit’s power is divided into two extremely simple components. The initial component is the 12-volt battery that supplies power to the circuit. The next section consists of a pair of a 6v zener diode and a resistor acting as a voltage regulator to supply half the Vdd voltage bias.

4.2.1.5  Circuit Analysis:

The circuit needs to be assessed in two different ways for its initial boosting stage. The initial method is for the 0 Hz DC bias voltage. The next one is intended for the 200 kHz alternating current (ac) signal.

The voltage bias cannot pass through the capacitor isolating it from the AC signal. It cannot connect to R1 either because the voltage on each side of the capacitor is equal. A 1μf capacitance was selected to achieve the necessary hard cutoff for DC signals for this design to function properly. This indicates that the bias goes through the operational amplifier in one direction. For this path to provide a stable output at Vout, Vout must be equal to Vb to stop current from going through the operational amplifier.

The AC signal can pass through the capacitor and connect to half of the R1 ground because the signals are opposite. Ri is not impacted by Rb since Rb is ten times larger than Ri and is connected in parallel with Ri. The resistance of Rb is 3 million ohms, while the resistance of Ri is 300 thousand ohms. This indicates that Rb does not lose any of the signal. This indicates that it functions like a standard non-inverting amplifier when in superposition. R1 equals 2kΩ and R2 equals 100kΩ to achieve a 100-fold increase in this portion.

The differential amplifier comes next. Because two inputs are being used at this stage, it is most effectively evaluated using superpositions. This step depends on utilizing a non-inverting amplifier and an inverting amplifier arrangement. Drawing an equivalence between the formulas (1+R2/R1) and (R2/R1) is crucial at this point. This indicates that to achieve optimal results, the greatest amount of boosting should be focused on this area. However, in the working model, R2 is a 10kΩ resistor while R1 is a 1kΩ resistor. These yielded a tenfold increase necessary for this portion, yet they were supposed to achieve a hundredfold increase.

Finally, the last step of boosting is examined in the same way as the previous one. It is divided into the superposition of voltage bias and the superposition of AC signals. It functions in a similar manner with equivalent constraints as the aforementioned circuit.

4.2.1.6 Final Circuit:

The image below depicts the final circuit using the components described above.

4.2.2 Transmit Signal Output:

It was found while testing that the transmission signal for the crystal would function at approximately 30 volts. The signal was much higher than all other recorded feedback. Because of this, a zener clipping voltage regulation arrangement was implemented to redirect the voltage away from the amplification circuit whenever it went above 3v or below -3v. Then, the microcontroller would be linked to this pathway to receive signals transmitted from a different pin.

4.2.3 ADC incorporation Circuit:

Setting up the signal for the ADC is the final step in the signal processing. The ADC is limited to a range of 0V to 5V, therefore the amplified circuit’s output signal was modified using a diode and voltage divider to shift the range from 0V to 12V with a 6V offset to 0V to 5V with a 2.5V offset. This fresh arrangement was subsequently sent to the ADC for conversion to the microcontroller.

4.2.4 Testing the circuit:

The circuit received a 12V input and a 100KHz, 1V signal. This signal made the output fluctuate from 0V to 12V. When given 0V and impedance at the input terminals, the signal stabilizes at a 6V output.

4.3   Software System Design:

The goal of the software being utilized is to gather the data received from the ADC and eventually provide valuable insights to the user utilizing voltage readings and the time intervals between them. The ultimate objective is to constantly measure the depth of the sea floor and identify marine objects beneath the data collection vehicle consistently. The depth information will be shown to the user, while a sophisticated algorithm will examine the marine floor’s shape to detect any artificial items that may have sunk. Additional information will be gathered from a separate device to associate every data received from the sensors with a geographical location. This will help in mapping the site of data collection, beneficial for users hoping to revisit the precise spot where a valuable object may have been misplaced. Every function of this system was developed using the adaptable Python programming language.

4.3.1 Depth Visualization and Analysis:

• 4.3.1.1  Data Acquisition from ADC:

To visualize depths and conduct analysis on underwater marine structures, voltage readings were gathered from the transducers and analyzed. The method used to accomplish this was to employ an ADC to sample the sonar signals sent and received through the amplification circuit. In the diagram provided below, the system’s wiring is illustrated.

The ADC was connected to the Raspberry Pi using white and black wires for the data transfer and clock pins. The ADC’s CONV pin was linked to the brown wire and managed by a randomly selected GPIO pin on the Raspberry Pi. The breakout box’s green wire was linked to the circuit’s ground. The breakout box had its blue and purple wires attached to the voltage-amplifying circuit’s input locations.

To enable the Raspberry Pi to collect these digital voltage measurements, a dependable communication channel between the ADC and the Raspberry Pi was set up due to the high volume of data readings that will be analyzed per second. An SPI communication line was implemented for data transfer purposes. The Raspberry Pi’s ‘sudo raspi-config’ command was used to activate the SPI busses.

An ADC data collection program in Python was created for the circuit. Initially, the required libraries were brought in. The list included:

• 7RPi.GPIO is utilized to allow the GPIO pin to switch between high and low states for ADC purposes.
• binascii is utilized for converting raw data obtained from an ADC into content that can be read by humans.
• spidev is employed to activate the SPI communication channel and set parameters like the number of bytes read and the speed of the clock.
• time is employed to generate an exact timestamp for every data reading gathered from the ADC.
• csv is employed to save all incoming data to a file for later examination.

Once all libraries were included, an SPI object was generated by utilizing the spidev.SpiDev() function. Next, settings were established for the SPI object including the amount of bytes to be read and the speed of the clock. After that, a while loop was started in order to begin the data collection process. This iteration began with lowering a GPIO pin for the ADC’s conversion. Next, data was retrieved by reading a response through the spidev library. Afterward, the CONV was raised to a high level. At last, the gathered information was saved to a new CSV document including an accurate time stamp. This time record will later be utilized to link data points with GPS coordinates gathered simultaneously.

4.3.1.2  Interpreting ADC Readings to Depth Values:

Once a mode of communication was established, the next part of the software system was able to start interpreting the incoming voltages readings from the transmitter and receiver. First, the program looked for a transmit signal which was recognized by a large voltage reading as the passive transducer that the ADC is drawing readings from will detect the strong send signal from the powered transmitting transducer that is inches to the side of it. In order to determine what such a reading from the ADC would look like for this scenario, a test was conducted in the pool.

First the Garmin chartplotter system was connected to one of the transducers which was placed at the surface of the pool as seen in the image below.

The Garmin chartplotter accurately determined the depth. The following image illustrates that the pool’s depth measured from the position of the transducer was 7.5 feet. The number was then utilized to determine the distance that a sonar pulse would cover, which is 15 feet or 4.572 meters. The speed of sound for sonar (1450 m/s) was utilized to calculate that the time for the transmitted pulse to be received would be 0.003153 seconds.

The measurement of flight time was then utilized to identify the attributes of the actual received signal. ADC data was gathered with the transducer staying in a fixed location.

Next, the signal was easily recognized by utilizing the timestamps linked to the gathered samples. This information was then utilized to determine the depth by identifying the reception of all other transmit signals.

After detecting the transmit signal, a timer variable was set to start calculating the round trip time for the signal to reach the ocean floor and return to the passive transducer. Once the signal is received and detected, the timer is halted and the variable will represent the duration of the signal’s round trip from the boat to the seafloor and back to the boat. This figure will be utilized to determine the depth at that location in the body of water under examination. Achieving this can be simplified by utilizing the speed of sound in water and multiplying it by the time elapsed variable. This distance is cut in half because only the distance between the boat and the marine floor matters, not the total distance traveled. The recently discovered depth value is included in an array data object that will be analyzed later to generate the profile visualization of the ocean floor.

4.3.1.3  Filtering Noise and False Return Signals:

The measurement of flight time was then utilized to identify the attributes of the actual received signal. ADC data was gathered with the transducer staying in a fixed location.

Next, the signal was easily recognized by utilizing the timestamps linked to the gathered samples. This information was then utilized to determine the depth by identifying the reception of all other transmit signals.

After detecting the transmit signal, a timer variable was set to start calculating the round trip time for the signal to reach the ocean floor and return to the passive transducer. Once the signal is received and detected, the timer is halted and the variable will represent the duration of the signal’s round trip from the boat to the seafloor and back to the boat. This figure will be utilized to determine the depth at that location in the body of water under examination. Achieving this can be simplified by utilizing the speed of sound in water and multiplying it by the time elapsed variable. This distance is cut in half because only the distance between the boat and the marine floor matters, not the total distance traveled. The recently discovered depth value is included in an array of data object that will be analyzed later to generate the profile visualization of the ocean floor.

4.3.1.4  Processing Collected Depth Values:

The upcoming part of the software system converts the array of depth values collected during a depth acquisition session into a 2D image of the marine floor that the boat traveled over. The task was accomplished through the use of the Python packages NumPy and MatPlotLib. The software progresses through each position in the array provided as the starting input, marks a point on a graph showing depth over time, and then links each plotted point with a line to reconstruct the ocean floor.

4.3.1.5  Detecting Man-Made Objects:

The last part of the software system is a program that examines the shape of the line representing the ocean floor to identify any artificial objects on the seafloor. In the initial iteration of the system, efforts were made to detect targets on the seabed. To achieve this, the program must first identify what it needs to search for. In a controlled setting, a depth acquisition session will be conducted in the absence of any targets. The information will lead to a straight line on the seabed map. Many conclusions can be made from the dataset including variations in the Y axis of plotted points over time, the slope between depth points, and the rate of change in the Y axis based on the depth measurement over time. Afterwards, the identical process will be conducted on an object located on the seabed, in an environment with little background noise. The identical data conclusions will be noted.

Once a map of the sea floor is created, the target detection software will analyze the mapped characteristics in comparison to those seen in both a vacant setting and with a target present. The software will examine all points and gradients to identify similar patterns signifying the presence of a target. One instance where the program might identify two sudden changes on the Y axis in a depth plot, resulting in a square or rectangular shape. Using the training data gathered, the system is able to identify when a lobster trap is present and will alert the user.

4.3.2 Acquiring and Integrating Additional Data

Although the system is effective for promptly determining if a man-made object is present in a setting, it becomes ineffective if the user cannot retrieve the items immediately and must come back to the location later. To allow the end user to do this, a GPS module will be integrated to record latitude and longitude data while the sonar system transmits pulses and computes depth readings. To get information from the GPS module, a serial communication connection was established between the GPS and the Raspberry Pi using the hardware UART GPIO pins on the device. Accomplishing this involves setting up the Raspberry Pi to allow hardware communication via serial, while also turning off shell and kernel messages. Afterward, the configuration file of the Raspberry Pi is modified in order to activate UART for all users. Administrative privileges are required to perform this task, which can be achieved by adding ‘sudo’ before the edit command. Afterwards, the libnfc library is developed to enable users to access NFC devices in userspace. Following some additional minor adjustments to set up the Raspberry Pi, the libnfc library is compiled and the GPS module is prepared for data collection.

To read the data coming in over the opened line of communication, the following code snippet can be run.

import time import board import busio

import adafruit_gps import serial

data_in = serial.Serial(“/dev/ttyS0”, baudrate=9600, timeout=10

      while(1):

while GPS.inWaiting()==0:

Pass NMEA=GPS.readline() print (NMEA)

As depth readings are being calculated they will be paired with an accurate location data point that the GPS module will constantly be read from the Raspberry Pi computer. In this case, a new data object will be created which consists of an array of arrays which is demonstrated below.

[[depth_data_0, gps_data_0], [depth_data_1, gps_data_1], … [depth_data_n, gps_data_n]]

In order to save time and system resources, the raw data string from the GPS module will be left as is until the program has collected all data points and needs to plot the points on a map. The raw GPS data will be brought into the data object as an NMEA string. This standardized format is composed of many characters and numbers that are meaningless to the human eye. A sample of one GPS reading consisting of its NMEA sentences are pictured below.

• $GPGGA,123519,4807.038,N,01131.000,E,1,08,0.9,545.4,M,46.9,M,,*47
• $GPGSA,A,3,04,05,,09,12,,,24,,,,,2.5,1.3,2.1*39
• $GPGSV,2,1,08,01,40,083,46,02,17,308,41,12,07,344,39,14,22,228,45*75
• $GPRMC,123519,A,4807.038,N,01131.000,E,022.4,084.4,230394,003.1,W*6A
• $GPGLL,4916.45,N,12311.12,W,225444,A,*1D
• $GPVTG,054.7,T,034.4,M,005.5,N,010.2,K*48
• $GPWPL,4807.038,N,01131.000,E,WPTNME*5C
• $GPAAM,A,A,0.10,N,WPTNME*32
• $GPAPB,A,A,0.10,R,N,V,V,011,M,DEST,011,M,011,M*3C
• $GPBOD,045.,T,023.,M,DEST,START*01
• $GPBWC,225444,4917.24,N,12309.57,W,051.9,T,031.6,M,001.3,N,004*29
• $GPRMB,A,0.66,L,003,004,4917.24,N,12309.57,W,001.3,052.5,000.5,V*20
• $GPRTE,2,1,c,0,W3IWI,DRIVWY,32CEDR,32-29,32BKLD,32-I95,32-US1,BW-32,BW- 198*69
• $GPXTE,A,A,0.67,L,N*6F
• $GPMSK,318.0,A,100,M,2*45

It is evident that the end user will not be able to understand any of this data when trying to figure out the location of their depth values. Nevertheless, a Python script can efficiently extract the latitude and longitude from the NMEA sentences at every specified moment. The most crucial information to uncover from the NMEA raw data will be this, however, other useful information may include time and velocity. The code examples provided can be used to extract latitude and longitude data and convert it into usable coordinates.

After acquiring the relevant coordinates, the data points showing a missing target can be plotted on a usable map of the region for the user to consult later on. Achieving this is possible by utilizing Google Earth along with a KMZ wrapper .xml file. A basic form of this covering can be observed underneath. A new function will be made to gather all latitude and longitude values, match them into their correct coordinates, and insert them into the KMZ file with a space in between and. Once all coordinates are included, a new KMS file will be stored in the system. The file can be uploaded to Google Earth by the end user, allowing them to pinpoint where they collected depth data and locate lost targets.

<?xml version="1.0" encoding="UTF-8"?>

<kml xmlns="http://www.opengis.net/kml/2.2">

<Document>

<Style id="yellowPoly">

<LineStyle>

<color>7f00ffff</color>

<width>4</width>

</LineStyle>

<PolyStyle>

<color>7f00ff00</color>

</PolyStyle>

</Style>

<Placemark><styleUrl>#yellowPoly</styleUrl>

<LineString>

<extrude>1</extrude>

<tesselate>1</tesselate>

<altitudeMode>absolute</altitudeMode>

<coordinates>

</coordinates>

</LineString></Placemark>

</Document></kml>

In the future, another function may be created that will automatically upload the KMZ file to Google Earth directly without any interaction from the end user.

4.4 Mechanical System Design:

The mechanical system is designed to integrate the transducers, chartplotter, electrical system, and software system on a single platform. This will enable all components to function together while floating on the water during testing. Two distinct mechanical systems were created to be used in various testing scenarios. One was intended for use as a fixed device in locations that do not require movement. The purpose of the second one was to create a mobile app to help map the floor of the testing environment.

4.4.1 Stationary Application:

A device was created and constructed for securing the position of the transducers during experiments conducted in controlled settings like a pool or bathtub. This would, in turn, result in less variation in transducer movement during testing, leading to more precise outcomes. The bracket was constructed with wooden boards, PVC pipes, and clamps. The picture below displays the perfect position for placing the bracket. Ideally, the base should be positioned at the perimeter of the test waters, like a pool, where the transducers were immersed in the water underneath.

The individual pieces used in the construction are as follows:

• 2, 24 inch 2×4
• 2, 12 inch 2×4
• 2, 12 inch 1.5 inch ID PVC
• 2, 28 inch 1 inch ID PVC
• 4, 1.75-inch to 2.75-inch pipe clamps

The pieces of lumber were fastened together using screws in the manner shown. One hole was drilled in each 12-inch PVC pipe, positioned 2 inches from the bottom. Drill holes every 6 inches in both 28-inch PVC pipes for adjusting transducers to different depths based on water level at the testing site. Next, a sensor was connected to every one of the extending PVC setups. In the end, the last two pipe clamps were fastened onto a 2×4 by using only one screw. These clamps were later utilized for fastening each system to the base of the platform.

The bracket was created with multiple degrees of freedom for each PVC-transducer system to compensate for potential variations in the locations or angles of the transmitted signals. In this way, the transducers can be rotated along the x, y, or z axis to determine the best position for signal emission from each of them. The reason for locating this spot is to ensure effective transmission and reception of signals between the transducers in order to obtain precise and clear experimental data.

The images below show the three degrees of freedom designed into the circuit system. 

The pieces of lumber were fastened together using screws in the manner shown. One hole was drilled in each 12-inch PVC pipe, positioned 2 inches from the bottom. Drill holes every 6 inches in both 28 inch PVC pipes for adjusting transducers to different depths based on water level at the testing site. Next, a sensor was connected to every one of the extending PVC setups. In the end, the last two pipe clamps were fastened onto a 2×4 by using only one screw. These clamps were later utilized for fastening each system to the base of the platform.

The bracket was created with multiple degrees of freedom for each PVC-transducer system to compensate for potential variations in the locations or angles of the transmitted signals. In this way, the transducers can be rotated along the x, y, or z axis to determine the best position for signal emission from each of them. The reason for locating this spot is to ensure effective transmission and reception of signals between the transducers in order to obtain precise and clear experimental data.

Shear Stress Calculation:

Step 1: Total Weight of PVC and Transducer:

Garmin CV52HW-TM Transducer: From website: m = 1.13kg
12in 1.25in ID PVC:
ID = 1.25in = 3.175cm; OD = 1.66in = 4.216cm; ρPVC = 1.45g/cm3; h = 12in = 30.48cm
V = πh(r12- r22) = π*30.48cm(2.108cm2-1.588cm2) = 184.03cm3
m1 = V*ρPVC = 184.03cm3* 1.45 g/cm3 = 0.267kg = 0.589lbs
28in 1in ID PVC:
ID = 1in = 2.54cm; OD = 1.315in = 3.34cm; ρPVC = 1.45g/cm3; h = 28in = 71.12cm
V = πh(r12- r22) = π*71.12cm(1.67cm2- 1.27cm2) = 262.75cm3
m1 = V*ρPVC = 262.75cm3* 1.45 g/cm3 = 0.381kg = 0.840lbs
Total Mass of Each PVC-Transducer System: 1.13kg + 0.267kg + 0.381kg = 1.778kg = 3.9lbs

Step 2: Free Body Diagram:

Step 3: Shear Stress Calculation:

#6 Screw Diameter = 0.0035m; Shear Strength of Steel = 200MPa
F = m*a = 1.778kg * 9.81m/s2
τ = (4F) / π(d)2 = (4*17.44N) / π(0.0035m)2 = 1.81MPa
FOS = τMAX / τ = 200Mpa / 1.81 MPa = 110.5
The analysis above showed that the shear stress on the mounting screw in each PVC-transducer system is significantly lower than the shear strength of steel. Therefore, it is safe to assume that the system is adequately supported by a single screw. The bracket’s placement on the edge of the experimental water is the focus of the second part of the analysis. Ideally, the 2×4 base’s weight should be sufficient to counterbalance the PVC transducer system without needing someone to stand on it. In this stage of the analysis, we aim to determine the necessary amount of the 2×4 base that must be at the edge to avoid the PVC transducer system from dropping into the water.

Static Analysis:

Step 1: Free Body Diagram:

In the diagram shown above, only half of the complete bracket system was represented, with the assumption that both sides experience the same forces. F1 is the force resulting from the weight of one 12-inch 2×4 placed at the back of the bracket base. F2 is the result of the 24-inch 2×4’s weight. The PVC-transducer system’s weight is represented by the force F in the shear stress calculation. Point A is the pivot point between the pool edge and the base of the bracket, while r indicates the distance from the pool edge to the end of the bracket.

The weight of the second 12inch 2×4 was seen as insignificant because it will be located near the pivot point, resulting in minimal impact on the overall system.

Step 2: Force Calculation:

2×4 Data from Home Depot: 96in (2.44m) = 9lbs (4.08kg)
4.08kg/2.44m = 1.67kg/m
0.610m * 1.67kg/m = 1.02kg (mass of 0.610m section)
F2 = m * a = 1.02m * 9.81m/s2 = 10N
0.1525m * 1.67kg/m = 0.254kg (mass of half of the 0.305 section in the back)
F1 = m * a = 0.254m * 9.81m/s2 = 2.5N
F = 17.44N

Step 3: Moment Analysis:

The moment analysis below was done to calculate the minimum value of r where the force F would not tip the system into the testing water.
2.5N(0.610m – r) + 10N(0.305m – r) > 17.44N(r)
1.525Nm – 2.5r + 3.05Nm – 10r > 17.44r
4.575Nm – 12.5r > 17.44r
4.575Nm > 29.94r
0.153m > r
Therefore, as long as the edge of the brack base does not overhang the edge of the testing water by greater than 0.153m or 6 inches, the base will provide enough counterweight to hold up the PVC-transducer systems.

4.4.2 Mobile Application:

A floatation device was created to support the chartplotter, circuit, and transducers for the sonar’s mobile app. This tool will enable these components to float in the test environment so the system can be moved over different targets in the experimental body of water. Ideally, once this is operational, the platform’s movement will provide an additional dimension for visualizing the target. Mostly made from PVC piping, this device also includes a wooden floor and foam walls to provide extra protection for the onboard devices from moisture while in motion.

                                                                   Figure 29: Buoyant Platform

Most of the boat’s base was built using PVC piping. Two PVC pieces measuring 3 inches by 14 inches were connected at a right angle to the base platform to enable the transducers to be mounted underwater. The PVC part of the platform is what enabled it to float in reality. The goal was to avoid water entering and sinking the platform by sealing every joint with a waterproof PVC adhesive. The platform was built by cutting 3-in ID PVC piping into the specified lengths. To facilitate future computations, the volumes of each segment are also taken into account. The formula V = π*r2 *h was utilized because buoyancy calculations are based on the sealed volume of the PVC cylinder. A radius of 0.0445m (1.75in) was utilized for the outer diameter of the 3in PVC.

                                                                  Table 1: Volume of Buoyant Platform

Afterward, the weight of each piece of PVC pipe was measured to be used in later calculations. It was assumed that a 3-in PVC pipe weighs 2.21kg per meter. To find the mass of each piece, the number for each segment was multiplied by the length.

                                                                   Table 2: Mass of Buoyant Platform

The total volume of the buoyant platform was calculated by excluding the volumes of the joints, as the pieces of piping typically fit very securely into the joints. Also, the weight of the floor and walls added at a later time were not taken into account for calculations.

While the platform was seen to float in the testing environment, further analysis was conducted to verify this. The maximum buoyant force was calculated by considering the total volume of the body and assuming full submersion. Next, this was measured against the overall gravitational force exerted on the platform because of its combined mass. The buoyant force, Fb, is equal to the submerged volume, Vs, multiplied by the density of water, D, in the formula shown below.

g stands for the gravitational constant.

Buoyant Force: Fb = Vs * D * g = 0.0232m3
* 1000kg/m3
* 9.81m/s2 = 227.88N
Gravitational Force from Mass: 11.61kg * 9.81m/s2 = 113.89N
The boat will float because the force from its mass is less than the maximum buoyant force. Then, they calculated the estimated actual buoyant force on the boat. The picture displayed below depicts the boat in the water before the floor and walls are installed.

                                                                        Figure 30: Boat in Water

From this, it was estimated that the entirety of the 14in pipes in the back and one-third of the 14in pipes in the front were submerged naturally. This was used to calculate the volume of material submerged. The actual buoyant force was then compared to the maximum buoyant force to see if the boat was large enough to support the circuit, chart plotter, and transducer.
Submerged Volume: (2*0.0022m3) + (0.33*(2*0.0022m3)) = 0.00585m3
Buoyant Force: Fb = Vs * D * g = 0.00585m3 * 1000kg/m3 * 9.81m/s2 = 57.39N
227.88N – 57.39N = 170.49N of maximum buoyancy to spare
From this calculation, 170.49N of force, or 17.38kg of mass, could potentially be added to the platform before it would be entirely submerged. Next, the force added by the transducers and chart plotters was determined to see if it was less than the maximum remaining force. Due to its small relative mass, the mass of the circuit was ignored.
From Garmin: mchartplotter = 0.77kg, mtransducer = 2.26kg F = (0.77kg + 2*2.26kg)*9.81m/s2 = 51.89N.

From this analysis, the mass of the transducers and chart plotters would only add an additional 51.89N of force to the boat which is well below the maximum capacity. As a result, it is confirmed that the platform will be able to support the system.
For the final aspect of the mobile platform, a cranking device was created. The purpose of this device is to attach to the front of the boat and then be placed on the edge of the testing environment. When turned on, this device would pull the boat with the attached system in a straight line across the water body at a constant speed. This constant speed would be used in the visualization program to create a third dimension. The device can be seen below. 

                                                                      Figure 31: Boat Pulling Apparatus

Using the drill would provide sufficient power to effectively tug the boat through the testing area. Before setting off, the torque limiter on the drill was adjusted to 1N*m to prevent potential damage if the boat exceeded the desired speed. Different speeds were tested in this range to see which one would keep the boat level while pulling it at a consistent speed. After visually confirming this, a notch was added to the drill trigger to replicate the speed for testing.

The main calculations made with the cranking tool focused on the idea that as the line wrapped around the spool, the boat’s speed would change due to the growing circumference. Measuring the spool’s diameter was done to verify the assumption, with measurements taken both with the line fully wrapped and extended to the boat’s position on the pool’s opposite side.

6.87cm is the size of the spool when the total amount of line is factored in, while the spool measures 6.37cm when the boat is placed on the opposite end of the pool.

When the boat reached the opposite end of the pool, a discrepancy of only 0.5cm was noticed.

the size of the spool’s width.

Then, the pool’s average boat speed was calculated. In order to accomplish this, the distance from the front of the boat to the other end of the pool was gauged. The timing of the boat’s movement from one side to the other was recorded and is presented in the table provided. The equation Velocity = Distance divided by Time was employed to determine the velocity.

                                                                      Table 3: Boat Velocity Calculations

The numbers generated in this test not only provided the average velocity of the boat but also confirmed that using the indentation made on the drill trigger pulled the boat along the water at generally the same rate. Next, the rotations per minute of the spool during the time the boat was being pulled were determined.
Revolutions of Spool per Movement Across Pool = 17.75
Average Time = 16.94s
RPM = 17.75 / 16.94s = 1.05RPM
Finally, to determine the influence of the changing spool diameter on the speed of the boat, the time was recorded between every full revolution of the spool as the boat was pulled across the pool. Two trials were completed, and the raw data from these trials is outlined in the table and graph below. For the graph, only data up to Revolution 17 was used.

5.0 Conclusion:

After finishing the project, a prototype was developed for a system that can determine depth and detect objects underwater. A specialized circuit was created and constructed to identify and boost sonar signals transmitted underwater. The circuit included an ADC which enabled data samples to be gathered on a computer. This computer utilized software to gather data samples from the circuit and subsequently determine the depth at each time interval. In addition, GPS coordinates were gathered using the same computer system, enabling the end user to pinpoint the location of the depth data collection. A testing device and floatation apparatus were built to enable testing, experimentation, and data collection in water. The flotation device had a specialized crank mechanism to navigate it at a consistent speed across the water, perfect for collecting data.

6.0 Future Work:

For future iterations of this system, students should focus on the following:

● Moving everything onto the boat in a watertight casing

In this project, the tests were conducted with the Raspberry Pi, chart plotter, amplification circuit, and power source placed outside of the testing water body. Nevertheless, all of these features would be integrated into the flotation device in the end product. This would offer a lone option for gathering underwater 3D data without needing access to shore. Moreover, the testing setting in this project was brief so that these objects could be stored on land. Future versions should have the freedom to move around in a significantly bigger body of water. Hence, a crucial aspect of this project moving forward is incorporating a water-resistant enclosure for these items while they are on the flotation device.

● Implementing underwater object detection program

An automated method to detect manmade structures on the seabed during a depth-finding scan would be beneficial for the end user. This could be accomplished by conducting depth-finding scans in a controlled setting with manmade objects such as boxes or crates and then utilizing the geometries of the mapped marine floor to distinguish these manmade structures due to their orthogonal geometries.

● Linking GPS to object detection

Another beneficial capability for the end user would be the option to detect and identify all man-made objects in a depth scanning process. This could be simple to achieve by comparing the timestamps of the ADC depth data with those of the GPS data. If the program detects a man-made object, it can search for the ADC readings linked to the depth readings, locate the timestamp attributes for these data points, and use the timestamp value to retrieve the GPS data collected at that specific moment.

● Providing a notification system when an object is detected

An intuitive user interface could also be implemented in order to make data presentation easy to understand by those who may not be familiar with the research tools used in this project. Such a UI could consist of a simple notification that would send a message to the user via sound or light to notify that a man-made object was detected on the ocean floor.

● Perform marine floor testing in larger bodies of water

To ensure the functionality of this system in practical situations, upcoming versions must undergo testing in larger and deeper bodies of water. Although controlled environments are ideal for research and development purposes, they do not replicate the actual conditions of commercial use in bodies of water such as oceans, rivers, or lakes.

Source: Underwater 3D Data Collection