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A Roadmap to Successful Sonar AI

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.


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.


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)

Close the Gaps! Airborne ASW Yesterday and Tomorrow

By Jason Lancaster, LCDR, USN


Anti-submarine warfare (ASW) is about putting sensors and weapons in place to detect and destroy submarines. The types of sensors have changed based on technological improvements and types of submarines, but the main principle is minimizing the sensor coverage gaps and engaging the submarine before it is within its weapons engagement zone (WEZ). Speed, endurance, and flexibility make aircraft excellent ASW platforms. It enables them to conduct wide-area searches and engage submarines before a submarine can attack.

Airpower is vital to protecting the center of gravity. In the Second World War, the European naval war’s center of gravity was the trans-Atlantic convoys that supplied the Allies’ war effort. The Allied struggle was to reduce air coverage gaps in the Atlantic to effectively protect convoys. In order to convoy ships across the Atlantic, the Allies had to close the gaps in air coverage. During the Cold War Era, the center of gravity was the power projection capability of the carrier. The challenge was to protect the carrier both for convoy protection and force projection. Today, the challenge to protect the carrier remains, and a dangerous new gap needs to be closed.

The Russian and Chinese navies have invested heavily in building quiet submarines capable of firing Anti-Ship Cruise Missiles (ASCMs) in excess of 200 nm. These missiles threaten our Carrier Strike Groups (CSGs) because the CSG lacks an organic capability to detect and engage these submarines outside of the submarines’ WEZ. This is not the first time that we have dealt with an increasingly dangerous submarine threat. Today, the U.S. center of gravity for naval combat remains the CVN. To defend the CVN or any high value vessels from submarines, we may find the answer to be similar to what it was in World War II and the Cold War. We can explore the U.S. Navy’s historical use of air power and technology to overcome submarine advantages and then explore future improvements to close the gaps using unmanned aircraft.  

The Second World War

The Battle of the Atlantic tested the Allies’ ability to defend trans-Atlantic convoys at points throughout the European Theater of Operations, from Archangel to Cape Town and the Panama Canal to the Suez Canal; convoys had to be protected from submarines. Allied victory in the Battle of the Atlantic was the result of the Allies’ ability to eliminate gaps in air coverage with long range air and carrier-based convoy escorts. The challenge for the Allies was to extend air coverage to cover the entire convoy route. The Allies closed air coverage gaps in three ways: they expanded the number of air stations, developed longer-range aircraft, and integrated the escort carrier (CVE).

In August 1942, aircraft were limited to proximity from the U.S., Canada, Iceland, Northern Ireland, Gibraltar, and the African coast. Air coverage decreased the number of attacks in the western approaches to the English Channel. However, the German U-boats continued their depredations farther to sea into an area where aircraft could not reach. The Navy had to continue to close coverage gaps.

In order to close gaps, the Navy went to work opening air bases around the Atlantic rim to expand air coverage. From Greenland to Brazil, the U.S. worked with host nations to build and develop airfields. Unfortunately, gaining permission to operate an airfield did not mean planes could start flying right away. For example, the Danish government in exile gave the United States permission to operate aircraft out of Narsarsuaq, Greenland in April 1941; VP-6 aircraft did not operate from there until October 1943. In Natal, Brazil, the Navy took over facilities that Pan Am had been developing in 1940, but the facilities did not officially become active until 1943. In the Caribbean, planes flew convoy routes from Coco Solo, Panama to Trinidad and on to San Juan, Puerto Rico.

Extent of Allied Air Coverage (Author Graphic)

The Navy acquired the bases to operate from, but to close the gaps, aircraft were required to patrol from those bases. The Navy began the war with long-range aircraft, but not the vast numbers required for the massive amount of ocean requiring protection. Thousands of hours of patrol time were required to detect a submarine, creating a massive demand for aircraft. Congress passed the Two Ocean Navy Act in 1940, but aircraft production and aviation training had to catch up to wartime demand. 49 fixed-wing patrol (VP) squadrons were formed in 1943 alone. The influx of new planes and aircrews allowed the Allies to swarm the Atlantic.

This influx of planes enabled the Navy to cover the Atlantic in aircraft and force the U-boats to change tactics. In 1940, U-boats had submerged at the first sight of an aircraft. Many of those aircraft lacked effective weapons to sink a U-boat. Improvements to depth charges, radar, and searchlights increased the kill count. By 1943, U-boats had been re-armed with quadruple 20 mm anti-aircraft guns and traveled the Bay of Biscay surfaced in packs for mutual defense against aircraft. Submarines shooting it out with aircraft resulted in the sinking of 34 submarines in the Atlantic in July 1943. Between August and December of 1943, the Allies flew 7,000 hours of patrols in the Bay of Biscay alone. 7,000 hours translated to 36 sightings, 18 attacks, and 3 kills. Although the number of sightings was low, the U-boats had implemented a policy of maximum submergence, reducing their ability to travel rapidly on the surface during daylight.

Despite increased bases and more aircraft, the center of the Atlantic remained out of reach to land-based aircraft. This gap was closed by escort carriers (CVEs). These aircraft carriers were converted from merchant ships and equipped with a flight deck and a composite squadron of approximately 20 carrier aircraft; typically F4F Wildcats and TBF Avengers. Escort carriers operated in two main modes; direct support to convoys flying patrols around the convoy searching for U-boats, or as the flagship of a hunter-killer squadron. Initially, the aircraft only flew daytime missions, but submarines would surface to recharge their batteries at night. The aircraft flying off escort carriers became the first to regularly fly night missions. Escort carrier groups sank 53 U-boats during the war, including 60 percent of all U-boats sank between April and September of 1944.

A torpedo plane approaches for a landing while USS Guadalcanal tows U-505 astern. (U.S. Navy photo)

By June 1944, U-boats operated primarily submerged utilizing snorkels. The Allies’ ability to build airbases, manufacture planes, and convert aircraft carriers from merchant ships had enabled them to patrol the entirety of the Atlantic, giving the U-boats nowhere to escape.  Staying submerged dramatically reduced submarine range and speed, and there were more U-boat losses than merchant ship losses by the end of 1944. Closing the air coverage gaps in the Atlantic enabled the United States to transport armies across the ocean, maintain the supply lines to the Soviet Union and Great Britain, and win victory in Europe.     

The Cold War

During the Cold War, the Navy focused resources into the ability to project power ashore by building carrier battle groups and operating them in the eastern Mediterranean and the high north. The Cold War carrier battle group had to contend with Soviet long-range naval aviation, as well as nuclear and diesel submarines. Protecting the carrier against nuclear and diesel-electric submarines required defense-in-depth to prevent coverage gaps where submarines could freely target the carrier.

In the early years of the Cold War, World War II-era aircraft carriers were converted to ASW carriers (CVS) and operated 20 S-2 Trackers and 16-18 ASW helicopters and their escorts. During the 1950s, the U.S. maintained 20 ASW battle groups composed of a CVS and escorts. Budget constraints, a focus on the Vietnam War, and the increasing maintenance costs of aging ships resulted in the decommissioning of CVSs through the late 1960s. To maintain carrier-based airborne ASW, the CV replaced an attack squadron (VA) with an air ASW squadron (VS).

Exercises such as Ocean Venture ’81 had demonstrated the Navy’s global reach and ability to place strike aircraft on the Soviet border undetected. The Soviets wanted to deny the eastern Mediterranean and the high north to carrier battle groups to protect the Soviet Union from these attacks. The Soviets’ primary means of denial were their massive submarine fleet and long-range aviation assets. The U.S. expected the Soviets to attack the convoy routes that would bring additional U.S. troops, equipment, and stores to Europe, as well as target the carrier battle groups.  

The U.S. developed an ASW system to protect both convoys and battle groups. Submarines and maritime patrol reconnaissance aircraft (MPRA) could patrol independently, but also received cueing from the Sound Surveillance System (SOSUS). SOSUS arrays stretched across the gaps that Soviet submarines would travel to reach the north Atlantic Ocean; from Bear Island to the Norwegian coast, and across the Greenland-Iceland-UK gaps (GIUK). These arrays were monitored by acoustic technicians and able to vector submarines and MPRA to pounce on Soviet submarines as they transited into the north Atlantic. These barriers formed the outer submarine defensive zones that would enable the U.S. to kill Soviet submarines in chokepoints. The role of these submarines and MPRA was sea denial.

A U.S. Navy Lockheed P-3C Orion from Patrol Squadron Eight (VP-8) “Fighting Tigers” flying over a Soviet Victor III-class submarine in 1985.(U.S. Navy photo)

Convoys would be supported by helicopter-equipped ASW frigates and destroyers and MPRA operating from bases in Canada, Iceland, the Azores, and the United Kingdom. The mission of these escorts was not to create permanent sea control, but to create a bubble of temporary local sea control that would enable the convoyed merchant ships to reach Europe without losses. Carrier battle groups would support these convoys, as required, to protect against air attacks, or would head to the Norwegian coast to conduct offensive operations against the Soviet Union.  

The purpose of the carrier battle group was sea control. The typical carrier battle group was composed of an aircraft carrier, 8-10 escorting cruisers, destroyers, and frigates, and the air wing. The carrier battle group utilized defense-in-depth to defend the carrier. The most distant ring was the inorganic theater ASW (TASW) fight utilizing the SOSUS network, MPRA, and submarines. The battle group did not lead this fight, but paid attention to it.  

Submarines that transited past the MPRA, submarine, and SOSUS barriers required the battle group’s anti-submarine warfare commander (ASWC) to defend the carrier. The 1980s battle group’s ASW plan was composed of three zones: the outer zone (100-300NM), the middle zone (30-70NM), and the inner zone (0-30NM). The battle group’s organic outer defense was composed of ASW helicopter-equipped frigates or destroyers with towed acoustic arrays. The VS squadron and helicopter anti-submarine squadron (HS) were to patrol the inner and middle zones, but maintained the ability to pounce in the outer zone, as required. The inner screen was composed of 3-4 destroyers or frigates utilizing active sonar. Active sonar was required because the carrier and its inner screen utilized speed and maneuver to minimize the ability of a submarine to target the carrier. The noise of speed negated passive tracking.

September 9, 1989 – A starboard quarter view of a Soviet Akula Class nuclear-powered attack submarine underway. (Photo via U.S. National Archives)

Victory for the TASW MPRA, submarine, and SOSUS team was the number of submarines destroyed. The battle group’s victory was defined avoiding an attack, whether that was from killing submarines, utilizing limiting lines of approach and maneuver, or defense-in-depth deterrence to prevent submarines from closing on the carrier. The Navy utilized multiple assets with different capabilities and limitations to prevent gaps in the carrier’s screen. TASW, multiple surface ships, CV, DD, and FF-based helicopters and ASW aircraft all contributed to the successful defense of the carrier. The skilled ASWC was able to balance the strengths and weaknesses of each part of the screen and keep the Soviet submarine away from the carrier.

ASW Today and Tomorrow

The threat of Soviet submarines seemingly disappeared with the collapse of the Soviet Union. Without the threat of Soviet submarines, U.S. interest in ASW withered. The nation’s peace dividend included the cancellation of the P-3 replacement aircraft, and the reduction of MPRA squadrons from 24 to 12 between 1989 and 1996. The remaining P-3s found their sensors optimized for detecting surfaced submarines and were useful to the Joint Force flying ISR missions over the Balkans and the Middle East. These missions sustained the reduced MPRA force through the budget cuts of the 1990s and the land combat-centric days of the War on Terror. The S-3B Vikings left their ASW role behind and performed mission tanking duties for F/A-18s before being prematurely retired, many with almost 10,000 flying hours left in them.  

In the 2010s, a new generation of ASW aircraft was flying. The P-8A Poseidon replaced the P-3C Orion and the MH-60R replaced the SH-60B and SH-60F. As witnessed during multiple ASW exercises, the combination of P-8As and MH-60Rs is nearly unstoppable. However, there is a clear capability gap at the strike group level. As a theater asset, the P-8s are limited in number, and fly missions across the fleet. The MH-60R has tremendous capability, but a limited range. It is not designed for area searching, but localizing a contact or conducting datum searches.

Full Spectrum ASW’s 9th thread is, “defeat the submarine in close battle.” With modern ASCMs and over-the-horizon targeting, the close battle is at least 200 nm from the strike group. The strike group must rely on the theater ASW commander to prosecute any modern submarines. While the strike group is important for the TASW commander to protect, TASW has a limited number of available submarines and P-8s and a multitude of submarines to prosecute. An organic aircraft capable of long-range ASW would enable the strike group commander to defend a larger strike group operating area, freeing TASW assets for threads 5 (Defeat submarines in choke points), 6 (Defeat submarines in open ocean), and 7 (Draw the enemy into ASW “kill boxes”).

Today, the CSG is composed of an aircraft carrier and three to five escorting cruisers or destroyers, which is half the ships of a Cold War-era Carrier Battle Group, and an air wing. The main organic ASW aircraft are MH-60Rs, helicopters with outstanding capabilities, but limited range. There are no organic ASW aircraft in the carrier air wing capable of searching, localizing, tracking, and engaging submarines beyond the submarine’s WEZ.  

MH-60Rs were not designed for area ASW searches and lack the endurance to search 200 nm from their ship. E-2 and EA-18G aircraft support the ASW fight with their capable radar and electronic warfare suites when the submarine is surfaced, or utilizing a periscope or radar. F-18s, C-2s, and MH-60Ss support primarily through visual search for submarines as they fly around the carrier. But searching for submarines visually or when surfaced are hardly ideal tactics.

Reducing the inner screen in order to get a ship out far enough to conduct a search in the outer zone is incredibly risky. A compelling solution is to establish an unmanned sea control squadron (VUS) squadron. These squadrons would provide Sea Combat Commanders with a dedicated medium-range ASW aircraft that would allow commanders to detect, classify, track, target, and engage submarines outside their WEZ. Everything the aircraft needs already exists. Equip a carrier-capable UAV with Forward Looking Infrared cameras (FLIR), AN/APS 153 radar, and ALQ-210 Electronic Support Measures systems from the MH-60R, LINK-16, active, passive, and Multi-Static Active Coherent (MAC) sonar buoys, and arm it with Mk 54 torpedoes and air-launched ASCMs.

This capable aircraft would directly support the Carrier Strike Group and enable it to engage submarines outside their WEZ. The technology exists. In order to protect the carrier today, the Navy needs to continue to close the gaps.

LCDR Jason Lancaster is a U.S. Navy Surface Warfare Officer. He has served aboard amphibious ships, destroyers, and as operations officer of a destroyer squadron. He is an alumnus of Mary Washington College and holds a Master’s Degree in History from the University of Tulsa. His views are his alone and do not represent the stance of any U.S. government department or agency.


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Featured Image: An S-3 Viking and A-6 Intruder from the USS John F. Kennedy (CV-67) fly over a Soviet Foxtrot class diesel submarine. (U.S. Navy photo)

Undersea Surveillance: Supplementing the ASEAN Indo-Pacific Outlook

By Shang-su Wu 

The recently announced Indo-Pacific Outlook by the Association of Southeast Asian Nations (ASEAN) at the 34th Summit indicates the Southeast Asian perspective on the evolving geostrategic environment. Unsurprisingly, ASEAN highlights cooperation, stability, peace, freedom of navigation and other values in the statement. The Outlook, however, leaves a question: how will ASEAN protect these values when diplomatic measures fail?

Under the ASEAN way, it would not be realistic to expect strong words such as those implying the use of force in any official statement, but member countries bordering critical straits could indirectly convey the message by demonstrating relevant defense capabilities. Among a variety of defense capabilities, tracking foreign submarines through enhanced undersea surveillance could be a relevant option.

Tracking Submarines

The major strategic significance of Southeast Asia in the Indo-Pacific region is mostly found in several critical sea lanes where various powers’ military assets travel through channels connecting the two oceans. Under the United Nations Convention on the Law of the Sea (UNCLOS), military vessels and aircraft enjoy the right of innocent passage through these sea routes, whether classified as international straits or archipelagic waters, and coastal countries track these movements. Modern technology makes it feasible for coastal states to readily track foreign military aircraft and surface vessels, a task that is more about safety than security. But tracking submerged submarines is another matter with a much higher barrier to entry.

In the face of complicated hydrographic conditions along with the improving stealth of submarines, there are high requirements for detection in terms of sonars, training, joint operations, and other elements of undersea surveillance. Therefore, successfully tracking submarines requires a high degree of military professionalism and capability. But once successfully tracked and trailed, a submarine receives a clear but private message of deterrence.

Silent Deterrence

This kind of covert deterrence would fit the geopolitical context in Southeast Asia. Firstly, it is generally legitimate for a littoral state to detect underwater entities because submarines should sail on the surface during innocent passage in territorial waters, while a submerged transit is acceptable under UNCLOS in passing sea routes and international straits. But only when a littoral state can identify the locations of foreign submarines transiting underwater can it determine whether UNCLOS is violated or obeyed. In other words, Southeast Asian countries have a sovereign right and legal obligation toward undersea surveillance. 

Tracking submerged submarines also presents a credible level of readiness for uncertainty. Overt exercises can be tailored for specific scenarios to prove certain levels of joint operations and other tactical skills, while bilateral and multilateral exercises highlight partnership, alliance, and other interstate security ties. Exercises are often much broader than the single capability of tracking submarines. Exercises, however, are either fully or semi-planned, and tracking foreign submarines is a truly dynamic encounter between two sides without an advance arrangement. Furthermore, Southeast Asian countries already have routinely conducted various bilateral and multilateral exercises with regional and extra-regional counterparts.

Tracking submerged submarines is usually beyond the microscope of conventional and social media, and can avoid the open hostility or other forms of public outcry that often transpire after close encounters between surface vessels. As the detecting side can deny any information on the tracking, publicity of the event would be more controllable compared with open statements or actions. For the country of the tracked submarine, such encounters are usually negative for national pride and military professionalism, so decision-makers would not have much incentive for revealing the encounter.  

Improving Hardware and Challenges Ahead

Since the end of the Cold War, Southeast Asian navies, particularly those of Indonesia, Malaysia, and Singapore, have built up their anti-submarine warfare (ASW) capabilities, including through several types of undersea sensors. These three countries have acquired survey vessels to establish their individual hydrographic databases. They have also procured state-of-the-art anti-submarine warfare helicopters such as the Super Lynx, S-70B, and AS-565MBe and deployed them on their respective frigates and corvettes which have towed or hull-mounted sonars. Furthermore, all three navies possess submarines to play the role of targets during training.

SOUTH CHINA SEA (June 18, 2013) A Royal Malaysian Navy Super Lynx prepares to land on the flight deck of USS Freedom (LCS 1) during deck landing qualifications (DLQs). (U.S. Navy photo by Mass Communication Specialist 1st Class Cassandra Thompson/Released)

Some characteristics impose challenges on the ability of Southeast Asian countries to track submarines. Large areas of territorial waters are natural obstacles for Malaysia and Indonesia. The numbers of maritime survey vessels they have in service are rather small for accumulating and updating their hydrographic data. By the same token, these two countries’ sensors and platforms, including ASW helicopters or ships, are likely not numerous enough to cover their broad territories or responsively deploy to where contacts are found.

Thanks to its tiny size, Singapore’s assets cannot be geographically diluted, but it shares other constraints with its neighbors, including a lack of fixed-wing ASW aircraft. The Indonesian CN-235 and the Singaporean Fokker-50 maritime patrol aircraft (MPA) only have limited ASW capabilities, and Malaysia’s smaller Beech-200 MPAs have no payload space for ASW weapons. Finally, operational experience is another common challenge for these three countries, as they began to introduce their sophisticated ASW assets mainly in the post-Cold War era where opportunity for practice was slim. 

Currently, the three navies are on a trajectory of improving their ASW capabilities, such as through the towed sonar arrays found in Malaysia’s upcoming frigates and Indonesia’s plan of building underwater surveillance systems. These efforts would gradually make tracking foreign submarines underwater more feasible in the foreseeable future.


Unlike in the Cold War-era, some Southeast Asian countries, especially these three bordering critical straits, do not have empty arsenals. Although their defense capability is still inferior to most extra-regional powers, some wise and tailored applications of their military assets would support ASEAN agenda’s beyond diplomatic and economic means. Successful tracking foreign submarines would make the ASEAN Outlook more valid in the Indo-Pacific geostrategic landscape.

Shang-su Wu is a research fellow at the S. Rajaratnam School of International Studies (RSIS), Nanyang Technological University in Singapore.

Featured Image: A Chinese submarine transits in the Yellow Sea (Wikimedia Commons)


Fiction Topic Week

By Evan D’Alessandro

The containers arrived at Norfolk early in the morning, with the snow a powdered sugar-like dusting on the trucks as they moved through the port. The darkness failed to hide their arrival from the Russians watching them through the hijacked security cameras. Another shipment in the cold weather of nondescript containers, their true propose not yet revealed. The containers had traveled for 36 hours to arrive on time and be loaded onto the requisitioned container ship MV Lt. Lyle J. Bouck. The watching Russians marked the containers as convoy supplies without a second thought, oblivious to what they had just missed.

Days before the containers were moved an AI had considered each ship’s cargo carefully. It speed, tonnage, fuel, acoustic signature, and survivability from a number of threats were all variables in the calculation. Ultimately, the AI decided that this convoy was not worth protecting. The cargo was all non-personnel, and the ships were old and only the commander’s ship was manned. The Navy had been stretched thin even with the Royal Canadian Navy and the Coast Guard ships that had been pressed into convoy duty. No ships would be assigned to protect them. They would be listed as unprotected, having to use the winter storms to shield themselves from satellites, as they attempted to dash across the Atlantic, praying for the best.

Vasily Sokolov read the report gleaned from a backdoor purchased off the Dark Web, checked the box for ‘no escort’ and moved on. He scrolled through the supply manifest slowly and then pulled up the satellite imagery for the ships, a satellite composite only four hours old.  The only visible armaments on the ships was the M109 Paladin, undoubtedly with its hypervelocity projectiles for air and missile defense. It sat atop a stack of red and blue containers, moored to them by large metallic brackets. A bulky cable snaked its way back to the superstructure of the ship, terminating in its dark underbelly. Vasily checked the ‘3D’ box and turned the ship, revealing a short ugly dome perched atop the superstructure, the predictive software pulling from known ship plans and previous satellite imagery. Quickly checking the projected dimensions on the dome against shipment records, Vasily confirmed that it contained the fire control radar that had been bolted on by techs the day before. With three of the ships in the convoy carrying a Paladin, it was hoped there would be some protection from hypersonic missiles. Vasily chuckled, as if missiles would be wasted on these low-value ships. A quick look at the aft decks of the ships confirmed that each was carrying two ‘Grasshoppers,’ the ASW drone that the Americans used. As he moused his way through the other ships, he could see Paladins emplaced onboard the other convoy ships, and prefabricated hangers being assembled on the back decks for the Grasshoppers. The tiny dots of technicians on his computer screen would be working through the night to get them finished in time to depart on their dangerous voyage.

The convoy sailed at 0800, picking up a low-pressure front that the predictive weather AI’s expected to turn to rain in the next 16 hours. The winter storms of the Atlantic were notorious, but the front seemed destined only for continual, dismal rain. The Grasshoppers went to work immediately, making sure that they weren’t picking up a tail when they exited the anti-drone net across the mouth of the harbor. The cycle of one hour on, two hours charging would continue as long as the weather permitted.

At 2100, in the darkness of the cloudy night, the containers were opened. Large cylinders were wheeled out and quickly put underneath the superstructure and covered in canvas. In the morning, they would be unseen by any satellite that managed to catch the convoy, and the Russians would be none the wiser. The convoy’s secret weapon, two Mk. 2 Autonomous Underwater Combat Vehicles (AUCVs) were prepared for battle. As their last restraint was being tightened the rain began, cloaking the convoy in its misty hold; the convoy would hide under this front for the rest of its journey.

Vasily Sokolov looked at the computer screen and leaned back. He stifled a yawn, and longed to go back to bed, but, no, there was a war going on, and his job needed to be done. His eyes ran down to the last box simply titled ‘Recommendations.’ Once again he paused, the convoy was equipped to deal with hypersonics, but not torpedo carriers, so that’s what he would recommend. One should be enough for an unarmed convoy, no, two for safety. Better safe than sorry his father had always said. Give them torpedo interceptors? No, the convoy wouldn’t be able to fight back, they only had 12 Grasshoppers. Better to load as many torpedoes as possible. His mind made up, Vasily Sokolov cracked his fingers and began to type.

The rain had begun to lessen in the middle of the Atlantic as the Captain arrived on the bridge from her all too short sleep. The USNR had called her up, and assigned her to what she considered to be little more than a oversized bathtub with propellers. The Captain’s voice echoed out across the bridge as she put on her VR display. “What does the report say?” The Tech looked out at the whitecaped waves as the threat report started to print out.

“Report for 41°37’41.5″N 31°33’22.5″W. Two Type 34 Autonomous Torpedo Carriers detected, no other threats at this time.”

 “Two Mother Hens” the Tech called out, “both are probably carrying a full load of eggs, no interceptors, the Autonomous Acoustic Monitoring AI predicts them to be here, but no one’s sure.” The Captain grumbled, she had never been comfortable with the idea of the football-sized drones floating through the water replicating SOSUS, but it was undoubtedly effective. “They went silent 4 or 5 hours ago, switched over to electric,” the Tech continued. “Any idea on what type of eggs?” the Captain asked with her light southern drawl. “Nope, the report has nothing on the torps,” the Tech replied wearily once again staring back out at the waves. The captain sighed as she stripped off the VR display, and went off to make up for her lack of coffee. For a brief moment her eyes gazed across the overcast rain and the Grasshoppers doing their job. There was nothing else she could do.

Ten miles out the Mother Hens were studying the acoustic signatures of the convoy. The onboard AI’s knew everything that Russian Naval Intelligence had gleaned about the convoy and were locked in deliberations. After a few minutes, they decided on a simultaneous pincer movement from the front and back as their plan of attack, and both slowly set off to get into attack position.

Grasshopper 4 was completing a set of passive dips on the north side of the convoy as droplets of rain pinged off its aluminum body. It had just popped up and moved 300 feet further north, covering the left flank of the convoy, and lowered its sonar when something unexpected happened. Imperceptible to the human ear, but detectable to the computer was a slight rumble. The computer reached a decision in seconds, deciding to stay put in the cold, grey rain, and requested Grasshopper 7 to immediately move into the area. Onboard the Bouck, a track popped up on the freshly-caffeinated Captain’s VR display, simply reading ‘possible threat.’ Beneath the waves of the Atlantic, the Mother Hen continued on its way oblivious to the threat above. Grasshopper 4 asked for permission to go to active sonar but the Captain denied it as  Grasshopper 7 sped its way towards Grasshopper 4, and the Bouck’s own Grasshopper 9 lifted off. The active could wait. As Grasshopper 4 waited it compared the rumble to previously recorded signatures in the Grasshoppers’ database, the VR display showing a rapidly increasing chance that the contact was a Mother Hen.  Calmly, the Captain watched the hostile track as the probability reached 60 percent, and then gave the order to fire.

Across the waves, Grasshopper 4 dropped the lower part of its body. The dull-grey, square casing discarded from the torpedo as it fell into the black water below, and the torpedo immediately went active. The Mother Hen detected the crash of debris ahead, and within milliseconds of hearing the first ‘ping,’ let off its own countermeasures. On the Bouck’s bridge the Captain looked on at the command map. Three of the four-noisemaker patterns were known, having been stolen from Russian firms under cyber espionage, and the torpedo immediately ignored them. The fourth noisemaker was unknown, and the Captain watched as the torpedo waivered for a heart-stopping second, then turned to chase the first Mother Hen.

The first Mother Hen had made it far too close to the convoy, nearly guaranteeing a hit with its torpedoes. The onboard AI considered trying to run but discarded the idea instantly. With an air of sadness, the first Mother Hen turned in towards the convoy and the oncoming torpedo, and unceremoniously fired all of its ‘eggs.’  A wave of  torpedoes lanced out in a spread: the Hen’s final gamble. As the torpedoes left, the two canisters on the Mother Hen’s back were blown upwards in a silver stream of bubbles towards the surface. One immediately broadcast the position of the convoy and the fate of the doomed Mother Hen. The second one popped out, and with an eruption of fire flew after Grasshopper 4. With little formality the missile closed, as Grasshopper 4 tried to hug the dark ocean for safety, before being turned into a bright ball of flame. The sorrow that was felt upon the loss of Grasshopper 4 was immediately overshadowed by the churning sea that signaled the death of the Mother Hen. Grasshopper 7 dipped into the cold waters and went active, ensuring that the Mother Hen was not playing dead. No return on the sonar. A confirmed kill.

Onboard the Bouck, the VR display changed to ‘threat destroyed.’ On the bridge, the Captain had already ordered a hard turn to starboard, turning parallel to the torpedoes and minimizing the convoy’s cross section. With the threat of incoming torpedoes and the possibility of a second Hen, the Captain unveiled her trump card. With an unceremonious crash into the Atlantic, the two carefully hidden Mk. 2 AUCV’s dropped into the waves, their long grey forms diving into the depths. All available Grasshoppers simultaneously rose from their charging ports in a frenzy of activity, as they moved across the convoy seeking out their enemies.

One of the Mk. 2’s now sat underneath the hull of the Bouck, trying to hide the fact that two were now in the water. The other Mk.2 assessed the incoming torpedo spread. The Mk.2’s AI pulled information from Grasshopper 7 and its own sensors, overlaying the convoy’s turn, and projecting forward. Three threats, the Mk.2 AI decided, and it dived and launched. Six ‘Silverfish’ torpedo interceptors raced out from the Mk. 2, closing in on the inbound torpedoes. The Captain looked on from the bridge. By the way the Mother Hen’s torpedoes were dodging, it was obvious they were outdated; clearly the Russians had underestimated the convoy’s defenses.

The Silverfish jabbered the whole way there, determining the Mother Hen’s torpedoes’ type and patterns. The first torpedo went left when it should have gone right, meeting its end in a mess of debris. The second torpedo dodged the first Silverfish, slipping through by diving at just the proper time, only to be met by the second Silverfish. The third torpedo dodged left, then right, the first Silverfish missing by mere inches, shortly followed by the second Silverfish mistaking a feint for a move and shooting underneath the torpedo.

The Mk. 2 looked on impassively, quickly calculating the chance of hitting the third torpedo, and launched a further three Silverfish. The torpedo was within 1000 feet and closing as the Silverfish streaked towards it, separated by mere seconds. The torpedo danced left, right, up, and down in an attempt to throw off the Silverfish gaining on it. But in the end it was not successful, the second Silverfish tearing its engines to pieces leaving it dead in the water. The Captain looked up coolly from the command map, only to hear klaxons blare.

The second Mother Hen had made it much closer to the convoy, slipping in through the convoy’s baffles while they were distracted, and finding itself a wolf among a flock of sheep. Sitting under the hull of one of its prey, it reached its decision and cut its engines, drifting slowly back, unseen in the darkness of the Atlantic.

The Captain sat up in shock as the VR display squealed an alarm, ‘FISH IN THE WATER! FISH IN THE WATER!’ and twisted around to see the tracks of four torpedoes from the second Mother Hen heading towards the Bouck and her sister ship the Sgt. William L. Slape. Behind her the Mk. 2 that had dealt with the initial torpedo barrage spit out the last of its 12 Silverfish at the new incoming wave, hoping that the interceptors would overtake the torpedoes before they hit. A Grasshopper also dropped down behind the convoy and went active, trying to acquire the threat. Within a second, another barrage of torpedoes from the second Mother Hen headed towards two other ships in the convoy, traveling underneath the water, preparing to pop up and hit the ship’s hulls perpendicularly.

The Captain waved her hand and the VR display stopped its alarms and calmly showed the tracks towards her convoy. Below her the fresh Mk. 2 was considering its options. It could try to destroy the torpedoes targeting the Bouck and the Slape, or it could go after the torpedoes targeting the ships farther forward. Grasshopper 5 noticed a lack of sound as one of the torpedoes targeting the Bouck stopped accelerating; it was now unguided and slowing as its propeller stopped, the watertight seals failing and the engine being swamped. The tracks of the Silverfish from the first Mk. 2 glowed green on the VR display, but it was more than clear that they would not stop the torpedoes in time.

The fresh Mk. 2 made its decision, and started to flip 180 degrees. Halfway through its turn it launched all 12 of its onboard Silverfish towardsthe torpedoes planning to pop-up, and brought its motors onto full. The Captain watched as her Mk. 2 launched its Silverfish, and her VR display show a 94 percent kill chance on the torpedoes targeting the ships farther down the line. The fresh Mk. 2 dropped both its torpedoes on the now acquired Mother Hen and pushed its engines to full, accelerating towards the torpedo.

The VR display shuddered as the rear end of the Bouck was lifted six inches from the water and its rear decks were covered in a spray as the Mk.2 met the oncoming torpedo. The torpedo tried to fight until the end, but the Mk. 2 imposed its bulk between the torpedo and the Bouck. An explosion was seen in the distance, the death of the second Mother Hen that had attacked. There was a second of calm then the Slape lifted several feet in the air as she too was hit. Two great spouts of water shot up from the side of the Slape as the torpedoes impacted just below the waterline. The VR display made an all-clear noise as the Silverfish intercepted and destroyed the remaining torpedoes, overtaking them and shattering them into a thousand pieces. Damage reports flooded in from the dying Slape. Like stricken rats, the Slape’s Grasshoppers, recharging from their last shift, fled the ship as it filled with water quickly shuttling to open charging ports on other convoy ships. The VR display marked the Slape as a loss, with a bright red outline, as the Grasshoppers buzzed, diligently searching for more enemies.

Behind the convoy a beacon popped up transmitting the location and death of the second Mother Hen. The Captain watched its progress as the noise of the fight slowly faded from her ears. Slowly the Mother Hen’s beacon was swallowed into the Atlantic, along with the shattered wreck of the Slape. The rain slowly picked back up in intensity as it covered the convoy with its grey cloak.

Vasily looked once more at his computer screen as it displayed the fate of the Mother Hens. “Spasibo”, he said to himself as a wry smile grew on his face, “Thank you for showing me your countermeasures.” He perched a cigarette between his smiling lips, reached out, and began to type, “To all AI Anti-Shipping Deployments….”

Evan D’Alessandro is a student at Luther College studying astrobiology, data science, and international relations. He enjoys military history and policy debate, and aspires to become a naval intelligence officer in the future. He can be contacted at evan.dalessandro@gmail.com.

Featured Image: Torpedo Exexutor, concept art by Markus Biegholdt, 3D art by Miroslaw Cichon.