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Emerging Tech Week

A Roadmap to Successful Sonar AI

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Emerging Technologies Topic Week

By LT Andrew Pfau

Even as the private sector and academia have made rapid progress in the field of Artificial Intelligence (AI) and Machine Learning (ML), the Department of Defense (DoD) remains hamstrung by significant technical and policy challenges. Only a fraction of this civilian-driven progress can be applied to the AI and ML models and systems needed by the DoD; the uniquely military operational environments and modes of employment create unique development challenges for these potentially dominant systems. In order for ML systems to be successful once fielded, these issues must be considered now. The problems of dataset curation, data scarcity, updating models, and trust between humans and machines will challenge engineers in their efforts to create accurate, reliable, and relevant AI/ML systems.

Recent studies recognize these structural challenges. A GAO report found that only 38 percent of private sector research and development projects were aligned with DoD needs, while only 12 percent of projects could be categorized as AI or autonomy research.1 The National Security Commission on Artificial Intelligence’s Final Report also recognizes this gap, recommending more federal R&D funding for areas critical to advance technology, especially those that may not receive private sector investment.2 The sea services face particular challenges in adopting AI/ML technologies to their domains because private sector interest and investment in AI and autonomy at sea has been especially limited. One particular area that needs Navy-specific investment is that of ML systems for passive sonar systems, though the approach certainly has application to other ML systems.

Why Sonar is in Particular Need of Investment

Passive sonar systems are a critical component on many naval platforms today. Passive sonar listens for sounds emitted by ships or submarines and is the preferred tool of anti-submarine warfare, particularly for localizing and tracking targets. In contrast to active sonar, no signal is emitted, making it more covert and the method of choice for submarines to locate other vessels at sea. Passive sonar systems are used across the Navy in submarine, surface, and naval air assets, and in constant use during peace and war to locate and track adversary submarines. Because of this widespread use, any ML model for passive sonar systems would have a significant impact across the fleet and use on both manned and unmanned platforms. These models could easily integrate into traditional manned platforms to ease the cognitive load on human operators. They could also increase the autonomy of unmanned platforms, either surfaced or submerged, by giving these platforms the same abilities that manned platforms have to detect, track, and classify targets in passive sonar data.

Passive sonar, unlike technologies such as radar or LIDAR, lacks the dual use appeal that would spur high levels of private sector investment. While radar systems are used across the military and private sector for ground, naval, air, and space platforms, and active sonar has lucrative applications in the oil and gas industry, passive sonar is used almost exclusively by naval assets. This lack of incentive to invest in ML technologies related to sonar systems epitomizes the gap referred to by the NSC AI report. Recently, NORTHCOM has tested AI/ML systems to search through radar data for targets, a project that has received interest and participation from all 11 combatant commands and the DoD as a whole.3 Due to its niche uses, however, passive sonar ML systems cannot match this level of department wide investment and so demands strong advocacy within the Navy.

Dataset Curation

Artificial Intelligence and Machine Learning are often conflated and used interchangeably. Artificial Intelligence refers a field of computer science interested in creating machines that can behave with human-like abilities and can make decisions based on input data. In contrast, Machine Learning, a subset of the AI filed, refers to computer programs and algorithms that learn from repeated exposure to many examples, often millions, instead of operating based on explicit rules programmed by humans.4 The focus in this article is on topics specific to ML models and systems, which will be included as parts in a larger AI or autonomous system. For example, an ML model could classify ships from passive sonar data, this model would then feed information about those ships into an AI system that operates an Unmanned Underwater Vehicle (UUV). The AI would make decisions about how to steer the UUV based on data from the sonar ML model in addition to information about mission objectives, navigation, and other data.

Machine learning models must train on large volumes of data to produce accurate predictions. This data must be collected, labeled, and prepared for processing by the model. Data curation is a labor- and time-intensive task that is often viewed as an extra cost on ML projects since it must occur before any model can be trained, but this process should be seen as an integral part of ML model success. Researchers recently found that one of the most commonly used datasets in computer vision research, ImageNet, has approximately 6 percent of their images mislabeled 5. Another dataset, QuickDraw, had 10 percent of images mislabeled. Once the errors were corrected, model performance on the ImageNet dataset improved by 6 percent over a model trained on the original, uncorrected, dataset.5

For academic researchers, where the stakes of an error in a model are relatively low, this could be called a nuisance. However, ML models deployed on warships face greater consequences than those in research labs. A similar error, of 6 percent, in an ML model to classify warships would be far more consequential. The time and labor costs needed to correctly label data for use in ML model training needs to be factored into ML projects early. In order to make the creation of these datasets cost effective, automatic methods will be required to label data, and methods of expert human verification must ensure quality. Once a large enough dataset has been built up, costs will decrease. However, new data will still have to be continuously added to training datasets to ensure up to date examples are present in the training of models.

A passive acoustic dataset is much more than an audio recording: Where and when the data is collected, along with many other discrete factors, are also important and should be integrated into the dataset. Sonar data collected in one part of the ocean, or during a particular time of year, could be very different than other parts of the ocean or the same point in the ocean at a different time of year. Both the types of vessels encountered and the ocean environment will vary. Researchers at Brigham Young University demonstrated how variations in sound speed profiles can affect machine learning systems that operate on underwater acoustic data. They showed the effects of changing environmental conditions when attempting to classify seabed bottom type from a moving sound source, with variations in the ability of their ML model to provide correct classifications by up to 20 percent.6 Collecting data from all possible operating environments, at various times of the year, and labeling them appropriately will be critical to building robust datasets from which accurate ML models can be trained. Metadata, in the form of environmental conditions, sensor performance, sound propagation, and more must be incorporated during the data collection process. Engineers and researchers will be able to analyze metadata to understand where the data came from and what sensor or environmental conditions could be underrepresented or completely missing.

These challenges must be overcome in a cost-effective way to build datasets representative of real world operating environments and conditions.

Data Scarcity

Another challenge in the field of ML that has salience for sonar data are the challenges associated with very small, but important datasets. For an academic researcher, data scarcity may come about due to the prohibitive cost of experiments or rarity of events to collect data on, such as astronomical observations. For the DoN, these same challenges will occur in addition to DoN specific challenges. Unlike academia or the private sectors, stringent restrictions on classified data will limit who can use this data to train and develop models. How will an ML model be trained to recognize an adversary’s newest ship when there are only a few minutes of acoustic recording? Since machine learning models require large quantities of data, traditional training methods will not work or result in less effective models.

Data augmentation, replicating and modifying original data may be one answer to this problem. In computer vision research, data is augmented by rotating, flipping, or changing the color balance of an image. Since a car is still a car, even if the image of the car is rotated or inverted, a model will learn to recognize a car from many angles and in many environments. In acoustics research, data is augmented by adding in other sounds or changing the time scale or pitch of the original audio. From a few initial examples, a much larger dataset to train on can be created. However, these methods have not been extensively researched on passive sonar data. It is still unknown which methods of data augmentation will produce the best results for sonar models, and which could produce worse models. Further research into the best methods for data augmentation for underwater acoustics is required.

Another method used to generate training data is the use of models to create synthetic data. This method is used to create datasets to train voice recognition models. By using physical models, audio recordings can be simulated in rooms of various dimensions and materials, instead of trying to make recordings in every possible room configuration. Generating synthetic data for underwater audio is not as simple and will require more complex models and more compute power than models used for voice recognition. Researchers have experimented with generated synthetic underwater sounds using the ORCA sound propagation model.6 However, this research only simulated five discrete frequencies used in seabed classification work. A ship model for passive sonar data will require more frequencies, both discrete and broadband, to be simulated in order to produce synthetic acoustic data with enough fidelity to use in model training. The generation of realistic synthetic data will give system designers the ability to add targets with very few examples to a dataset.

The ability to augment existing data and create new data from synthetic models will create larger and more diverse datasets, leading to more robust and accurate ML models.

Building Trust between Humans and Machines

Machine learning models are good at telling a human what they know, which comes from the data they were trained on. They are not good at telling humans that they do not recognize an input or have never seen anything like it in training. This will be an issue if human operators are to develop trust in the ML models they will use. Telling an operator that it does not know, or the degree of confidence a model has in its answer, will be vital to building reliable human-machine teams. One method to building models with the ability to tell human operators that a sample is unknown is the use of Bayesian Neural Networks. Bayesian models can tell an operator how confident they are in a classification and even when the model does not know the answer. This falls under the field of explainable AI, AI systems that can tell a human how the system arrived at the classification or decision that is produced. In order to build trust between human operators and ML systems, a human will need some insight into how and why an ML system arrived at its output.

Ships at sea will encounter new ships, or ships that were not part of the model’s original training dataset. This will be a problem early in the use of these models, as datasets will initially be small and grow with the collection of more data. These models cannot fail easily and quickly, they must be able to distinguish between what is known and what is unknown. The DoN must consider how human operators will interact with these ML models at sea, not just model performance.

Model Updates

To build a great ML system, the models will have to be updated. New data will be collected and added to the training dataset to re-train the model so that it stays relevant. In these models, only certain model parameters are updated, not the design or structure of the model. These updates, like any other digital file can be measured in bytes. An important question for system designers to consider is how these updates will be distributed to fleet units and how often. One established model for this is the Acoustic- Rapid COTS Insertion (ARCI) program used in the US Navy’s Submarine Force. In the ARCI program, new hardware and software for sonar and fire control is built, tested, and deployed on a regular, two-year cycle.7 But two years may be too infrequent for ML systems that are capable of incorporating new data and models rapidly. The software industry employs a system of continuous deployment, in which engineers can push the latest model updates to their cloud-based systems instantly. This may work for some fleet units that have the network bandwidth to support over the air updates or that can return to base for physical transfer. Recognizing this gap, the Navy is currently seeking a system that can simultaneously refuel and transfer data, up to 2 terabytes, from a USV.8 This research proposal highlights the large volume of data will need to be moved, both on and off unmanned vessels. Other units, particularly submarines and UUVs, have far less communications bandwidth. If over-the-air updates to submarines or UUVs are desired, then more restrictions will be placed on model sizes to accommodate limited bandwidth. If models cannot be made small enough, updates will have to be brought to a unit in port and updated from a hard drive or other physical device.

Creating a good system for when and how to update these models will drive other system requirements. Engineers will need these requirements, such as size limitations on the model, ingestible data type, frequency of updates needed by the fleet, and how new data will be incorporated into model training before they start designing ML systems.

Conclusion

As recommended in the NSC AI report, the DoN must be ready to invest in technologies that are critical to future AI systems, but that are currently lacking in private sector interest. ML models for passive sonar, lacking both dual use appeal and broad uses across the DoD, clearly fits into this need. Specific investment is required to address several problems facing sonar ML systems, including dataset curation, data scarcity, model updates, and building trust between operators and systems. These challenges will require a combination of technical and policy solutions to solve them, and they must be solved in order to create successful ML systems. Addressing these challenges now, while projects are in a nascent stage, will lead to the development of more robust systems. These sonar ML systems will be a critical tool across a manned and unmanned fleet in anti-submarine warfare and the hunt for near-peer adversary submarines.

Lieutenant Andrew Pfau, USN, is a submariner serving as an instructor at the U.S. Naval Academy. He is a graduate of the Naval Postgraduate School and a recipient of the Rear Admiral Grace Murray Hopper Computer Science Award. The views and opinions expressed here are his own.

Endnotes

1. DiNapoli, T. J. (2020). Opportunities to Better Integrate Industry Independent Research and Development into DOD Planning. (GAO-20-578). Government Accountability Office.

2. National Security Commission on Artificial Intelligence (2021), Final Report.

3. Hitchens, T. (2021, July 15) NORTHCOM Head To Press DoD Leaders For AI Tools, Breaking Defense, https://breakingdefense.com/2021/07/exclusive-northcom-head-to-press-dod-leaders-for-ai-tools/

4. Denning, P., Lewis, T. Intelligence May Not be Computable. American Scientist. Nov-Dec 2019. http://denninginstitute.com/pjd/PUBS/amsci-2019-ai-hierachy.pdf

5. Hao, K. (2021, April 1) Error-riddled data sets are warping our sense of how good AI really is. MIT Technology Review. https://www.technologyreview.com/2021/04/01/1021619/ai-data-errors-warp-machine-learning-progress/

6. Neilsen et al (2021). Learning location and seabed type from a moving mid-frequency source. Journal of the Acoustical Society of America. (149). 692-705. https://doi.org/10.1121/10.0003361

7. DeLuca, P., Predd, J. B., Nixon, M., Blickstein, I., Button, R. W., Kallimani J. G., and Tierney, S. (2013) Lessons Learned from ARCI and SSDS in Assessing Aegis Program Transition to an Open-Architecture Model, (pp 79-84) RAND Corperation, https://www.jstor.org/stable/pdf/10.7249/j.ctt5hhsmj.15.pdf

8. Office of Naval Research, Automated Offboard Refueling and Data Transfer for Unmanned Surface Vehicles, BAA Announcement # N00014-16-S-BA09, https://www.globalsecurity.org/military/systems/ship/systems/oradts.htm

Featured Image: Sonar Technician (Surface) Seaman Daniel Kline performs passive acoustic analysis in the sonar control room aboard the guided-missile destroyer USS Ramage (DDG 61). (U.S. Navy photo by Mass Communication Specialist 2nd Class Jacob D. Moore/Released)

Global Analysis

Anti-Submarine Warfare (ASW) – the Heart of Surface Warfare

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By Captain Charlie Williams, U.S. Navy 

Since the end of the Cold War, the Surface Navy has supported contingency operations around the globe, and done so exceptionally. Even so, some would argue that these operations have drawn us away from our basic warfighting skills – skills that have defined the United States as the world’s elite Surface Navy over the past 70 years.

In the area of Anti-Submarine Warfare (ASW), the Surface Warfare Officer (SWO) community must recapture that professionalism and intensity that drove us to become the premier ASW force in the 1970s and ‘80s — demonstrated time and again against the Soviet threat. We must dominate our Inner Screen while also correctly expanding our reach in the undersea domain.

Honed by years of experience and technological leaps, first in World War II and then again during the Cold War, ASW tactics and technology aligned with Anti-Surface Warfare (ASuW) as the focus of the destroyer force. With the collapse of the Soviet Union, the submarine threat diminished and the Surface Warfare community shifted our focus from ASW to support other emerging mission areas. The Surface force created VBSS boarding teams and manned crew-served weapons out of hide, and also honed our ability to execute Tomahawk strike missions to a fine and precision art form, while other nations instead determined to field a credible undersea force invested in capability and capacity.  

As a result, our ASW proficiency suffered, as our ASW experience-based knowledge dwindled to the point where the Navy would have been challenged against a modern-day subsurface threat. We lost our foil and also our operational training opportunities that presented themselves every time our ships got underway. We no longer had the opportunity to train in real world track and trail events against a YANKEE, NOVEMBER, or VICTOR Class Submarine from the moment we left the sea buoy. Those opportunities were especially important in maintaining our complex skills required in the ASW arena, such as passive target motion analysis and active Convergence Zone (CZ) search and detection.

Today, with our renewed emphasis and shift to the Pacific, the Surface Navy must reclaim the ASW battle space if we are going to be successful in this new era.

The Evolving Threat

Recognizing the disruptive challenge submarines pose to our aircraft carriers and other high value assets, China, North Korea, and Iran have invested in a significant undersea capability and capacity. Real world events in the Western Pacific and in the Persian Gulf serve as regular examples as to why the United States must maintain the resolve to invest in our Surface Navy to maintain a preeminent ASW capability. From the Surface ASW perspective, quieter submarines, emerging submarine tactics, and advanced weapons are potential challenges to our Carrier Strike Group (CSG) and Expeditionary Strike Group (ESG) operational concepts – and to the Surface force’s ability to own the Inner Screen and defend the Strike Group. To meet this evolving threat and maintain our naval dominance — We Must Adapt.

Surface ASW Response

Recognizing the need to counter the emerging threat, the Surface Navy began using a method similar to the commercial sector allowing for timely and affordable modernization of our ASW capability with Commercial-Off-The-Shelf (COTS) hardware systems and Open Architecture (OA) software. We recognize our ASW operators require the best and most advanced tools available – and we have invested heavily in every aspect of that ASW kill chain.

Improvements include hardware and software upgrades to kinetic weapons such as the advanced Mk 54 Lightweight Torpedo that integrates with the MH-60R multi-mission helicopter; sensors, such as the Multi-Function Towed Array and the SPQ-9B Periscope Detection and Discrimination kit; advanced processing and display capabilities to increase operator recognition while leveraging the skill sets already developed in our Sailors; as well as the high fidelity trainers being delivered to the fleet today.

Today, 30 SQQ-89 A(V)15 ASW Combat Systems have reached the fleet and by 2020 there will be 64.   That steady increase in capacity requires an equally steady application of financial resources, through which the Surface community has approached development of the ASW Combat System in a similar fashion to the continuous development and improvement of the AEGIS Combat System.

With the emergence of the Littoral Combat Ship (LCS) in the fleet, ASW operations will expand beyond the Arleigh Burke-class Destroyer (DDG) and Ticonderoga-class Cruiser (CG) in both an individual and additive manner. LCS, well suited to succeed in challenging littoral environments with its ASW Mission Package, will also support ASW escort missions. The combination of LCS and CRUDES ASW capabilities will significantly expand the reach of the ASW force, allowing it to operate in distinctly different environs in the open ocean and littorals while also enabling the DDG or CG to engage in other mission areas without sacrificing an ASW asset. The ASW capability realized by combining a Variable Depth Sonar with a Multi-Function Towed Array, plus the processing and display functionality of the A(V)15 system and the engagement capability of the onboard helo, provides a return on investment many times over.

Providing the US Navy with ASW capability takes more than hardware and software. Essential to successful ASW is the shipboard team that can exploit the capability being delivered as well as understand the environment affecting their system. This has always been true – but given the technologies being employed in today’s systems, and the threat we face at sea, our Sailors must be more technically and operationally savvy than ever before.

This requirement demands more time, both in port and at sea, to train in the skillsets unique to ASW. High fidelity unit level and shore based trainers delivered to the fleet facilitate this training, and add an element of at-sea realism to challenge even the most experienced operators. For the first time, the Navy can conduct high quality training both underway and in-port thru the A(V)15’s high fidelity Surface ASW Synthetic Trainer (SAST). SAST is also being integrated into a new shore based trainer to allow realistic watch team in-port training tailored to the specific skillset needed.

Understanding how the environment impacts your craft is critical to successful employment of your systems. Our schoolhouse training is being tailored to more effectively deliver basic and advanced operator and employment training. The Navy is also reinvigorating the Afloat Training Groups (ATG) with knowledgeable experts – they will be the key enablers, helping our young operators translate the schoolhouse training into operational experience with the necessary skills of this core competency.

ASW Command & Control and Today’s Inner Screen

An important element of owning the inner screen has been our partnering with other communities in the more distant ASW fight. The DESRON Sea Combat Commander embarked in the aircraft carrier (CVN) owns the Strike Group ASW problem, and they work that challenge in company with the Theater ASW Commander to coordinate what has become a broader definition of that inner screen’s boundary. Previously defined by the torpedo danger zone and our own acoustic detection ability, today’s inner screen has expanded based on the evolved submarine and longer range threats, and also a more diverse and more capable portfolio of our own CSG assets. This theater level of coordination requires a modernized set of tools including the Undersea Warfare Decision Support System (USW-DSS), and also a more agile and ready surface ASW force. Surface Navy’s continued investment in ASW is integral to furthering that coordination and enabling our success at sea.

Conclusion

Tactical ASW superiority is a critical enabler to maintain Forward Presence and Sea Control, and support Power Projection and Deterrence. This begins with owning the CSG’s Inner Screen, and enabling the broader ASW environment through coordinated operations with the Theater ASW Commander. Surface Warfare is perfectly postured to lead, plan and execute that Inner Screen, and use our capacity, on-station time, and command and control ability as enablers in the larger, theater ASW fight. Our investments in systems, training, and people have positioned us to reassert our mastery of this critical warfighting capability. The time is now for the Surface force to rededicate itself to this most central of missions. After all, the world’s most lethal power projection Navy cannot do its job if the water it operates in is threatened from below.

Captain Charlie Williams is the Deputy for Weapons and Sensors, Surface Warfare Directorate (N96). He commanded USS FIREBOLT (PC 10), USS STETHEM (DDG 63) and Destroyer Squadron FIFTEEN (CDS-15). As the Commodore in CDS-15, he served as the GEORGE WASHINGTON Strike Group Sea Combat Commander and Strike Force ASW Commander, and subsequently served as the Seventh Fleet Chief of Staff.