Category Archives: Emerging Tech Week

Responding to the Proliferation of Uninhabited Underwater Vehicles

Emerging Technologies Topic Week

Sections of the following article are adapted from a forthcoming master’s degree thesis, titled The Hunt for Underwater Drones: Explaining the Proliferation of Uninhabited Underwater Vehicles

By Andro Mathewson

In late May 2021, the Israeli armed forces destroyed an armed underwater uninhabited vehicle (UUV)1 operated by the terrorist group Hamas. This kamikaze-UUV was used in an attempt to attack Israeli offshore gas and oil installations, which Hamas had unsuccessfully targeted in the past using rockets and uninhabited aerial vehicles (UAVs). This is possibly the first use of an armed UUV by a non-state actor, but UUVs have been in use since the 1950s, with the United States and Russia leading the charge. UUVs are now owned by over fifty nations across the world. Understanding why and how this technology proliferates is crucial to recognizing the role of such new technologies in international security and preparing effective responses. Based on this common understanding, the international community can counter further UUV proliferation by establishing a framework of norms and agreements, while security forces and military industries can focus on advancing effective counter-UUV technology.

Why Examine the Proliferation of UUVs?

UUVs are becoming an important tool within the realm of international security. Naval forces across the world are quickly developing and acquiring a variety of UUVs due to their furtive nature, dual-use capabilities, and multifaceted functionalities. While the technology is still in relatively early development stages and leaves much to be desired, UUVs have quickly become an integral element of modern navies but also appear in the arsenals of lesser developed armed forces and non-state actors due to their utility as an asymmetric tool for sea denial. With advancements in intelligence gathering, surveillance, and reconnaissance technologies, UUVs are becoming essential assets in the maritime forces of states across the world. Although still predominantly used in an unarmed and surveillance capacity, UUVs have recently also been both adapted and designed to carry explosive ordnance and act in an offensive capacity. While the United States and Russia are at the forefront of UUV development, over fifty other states have either developed or acquired UUVs, as the following map shows.  

Countries in possession of UUVs as of May 2021.2

There is also considerable interest in underwater drones and their diverse applications from militaries, private corporations, civil society organizations, and journalists alike.3 Their broad applications explain why the global UUV market size is projected to grow from USD 2.0 billion in 2020 to USD 4.4 billion by 2025. Despite the increasing interest in UUVs, many commentaries about their proliferation and use are based on speculation rather than on empirical analysis. Finally, examining the early proliferation of UUVs offers opportunities to explore, in-depth, the initial stages of a technology’s adoption by actors in the international arena, make predictions for the future, and prepare effective responses. While several of the patterns identified in this article might not persist moving forwards, it is nonetheless an opportunity to attempt to understand the wider motivations of governments and decision-makers on a global scale, including the role of security alliances, conflict, geography, economics, and international law.

UUV Proliferation

While at least 30 states have the indigenous capacity to manufacture UUVs, at least 55 states own or have previously owned UUVs.4 This demonstrates that there has been significant technology transfer and diffusion between states. UUVs, and the majority of the technologies they incorporate, are fundamentally dual-use, and the export thereof is often restricted by states and allowed only in a very small set of circumstances. For example, in 2009, the Egyptian Navy signed a deal under the United States Foreign Military Sales program for the delivery of  the U.S.-based Columbia Group’s Pluto Plus UUV system, intended primarily for mine identification and destruction. More recently, in 2016, the United States donated two Remus autonomous underwater vehicles to the Croatian Navy to upgrade their countermine capabilities. While the majority of UUV proliferation is based on such authorized transfers between nations and global corporations or domestic development, there have been numerous cases of unwanted UUV technology transfer through smuggling, intellectual theft, and capture.

There are at least four documented cases of UUVs being seized either by nations or non-state actors. Perhaps the most prominent example is that of China seizing a USN UUV in the South China Sea in late 2016. However, this is not how China first acquired UUV technology, yet it is a possibility that the Chinese Navy deconstructed the UUV to understand and reconstruct the technologies within. While China later returned this drone, it had previously been able to smuggle protected American UUV technology via middlemen out of the United States. Other examples include the capture of a US Remus UUV by Houthi forces off the coast of Yemen in 2018, the seizure of an American early-model mine reconnaissance UUV in 2005 by North Korea, and the capture of a Chinese underwater glider by Indonesian fishermen in 2020. While it remains unknown if these captured UUVs were later remodeled to be operational by their new owners, these incidents showcase both a lesser-known method of technology proliferation and an inherent vulnerability of UUVs.

The legal status of UUVs is a factor that has presently had little influence on their proliferation, partially due to their relative novelty in the international arena as well as due to the currently very unclear legal boundaries concerning unmanned underwater vessels. However, due to the ability of regulatory systems and international law to limit said proliferation or direct it solely to allied states, essentially weaponizing both limitation and regulation, this unclarity is unlikely to continue. Additionally, the distinctive ethical character of war at sea generates several novel ethical dilemmas regarding the design and use of UUVs, which have yet to be answered by international law but certainly require attentiveness.

Country Likelihood of UUV Adoption
Romania .886
Libya .812
Chile .780
Slovenia .751
Argentina .692
South Africa .653
Algeria .588
Cyprus .559
Ukraine .553
Iraq .462


Keeping track of new government acquisitions of UUV technology is an important first step in developing adequate responses. Thus, looking to the future, the database created for this article and the subsequent analysis thereof can help identify possible future adopters of UUVs.5 While exact foretelling is nigh impossible, the following table lists the ten most likely future adopters of UUV technology based on the author’s model.   The majority of the nations listed have extensive military requirements. As UUVs become less cost-prohibitive and countries become wealthier, their proliferation may reach a tipping point where they become a widespread and almost ubiquitous technology, possibly following the route of UAVs, which are now present in almost every military across the globe. One other possible explanation for the future acquisition of UUVs by these listed states is their involvement in ongoing maritime disputes as UUVs are useful tools for monitoring vessel movements in contested spaces.

Responses to UUV Proliferation

Due to their relative novelty, both responses to their use and mitigation strategies are presently scarce. Countering global UUV proliferation should be an imperative for the United States Navy, its allies, and international organizations alike. Despite the clear recent increase in proliferation over the past decade, there are currently no national or international agencies in charge of a response to military purpose UUVs, while their ambiguous legal status has led to a de-facto underwater arms race. Nevertheless, there are two possible answers to these challenges: risk mitigation and counter-UUV technology. However, a dual-pronged approach addressing both simultaneously will most likely have the most effective results.

The first option relies on a rules-based international system and the adherence of states to international agreements and regulations. Risk mitigation strategies attempt to minimize the risk of conflict through international cooperation. In the case of military technologies, this is primarily via arms control agreements, the effectiveness of which is hotly contested. While arms control has been somewhat effective for several weapons, such as cluster munitions, its ability to restrict the proliferation of other uninhabited vehicles, such as aerial drones, has been generally deemed unsuccessful. Similar to UAVS, the place of UUVs in the international legal framework is highly uncertain. Many issues remain unanswered: Is a UUV part of its state of origin and thus immune from legal seizure by other nations? Should they operate only on the surface in another nation’s territorial seas? Can it legally operate there at all?  (This is only a snippet of the many questions on UUV legality).

Deciding upon the legal status of UUVs in both domestic and international law is crucial for the security of states and the reduction of risk in the international arena. For example, classifying UUVs as ships or extensions thereof would categorize them under the rules of the United Nations Convention on the Law of the Sea (UNCLOS). This would allow UUVs to act correspondingly in the regions of the sea as determined by UNCLOS, illuminating where they may be legally deployed and for what reasons. Within the different zones, states could apply the rules currently affecting maritime vessels to UUVs, restricting the available legal actions of the UUV-controlling state. However, UNCLOS is not inviolable. Amongst many others, the United States has not ratified UNCLOS, reducing its coercive power. Many other states, including Russia and China, often criticize and neglect its stipulations. International law enforcement is also often ineffectual. Thus, although enforcing UUV use under the clauses of UNCLOS could alleviate some tensions, it is far from a panacea. Consequently, states must also develop more reliable defensive strategies and technologies to thwart antagonistic UUV deployments.

The development of counter-UUV technology is in its infancy, primarily due to two factors: the novelty of UUVs and the fact that they are predominantly still unarmed and used mainly for surveillance and intelligence gathering. However, the sooner the United States and its allies invest in and develop effective counter-UUV technologies and strategies, the more prepared they’ll be more future encounters. Due to the dual-use nature of UUVs, the true intentions behind their deployment are almost indistinguishable. Thus, states must prepare an extensive response toolkit, which requires both economic and political investments. Countering a technologically advanced threat requires the development of new defense mechanisms. In the case of UUV’s this could be new countermeasure methods of detection, tracking, and tracking – for example – acoustic or magnetic tripwires, to determine underwater movements through sensitive passages like harbors or straights. Another option is a more aggressive approach, such as the development of new systems to capture or outright destroy UUVs operated by adversarial states, including more precise torpedoes or more advanced naval mines capable of targeting and destroying UUVs.


The current status of aerial drones and their widespread use across the world offers militaries, policymakers, and international organizations the opportunity to prevent a similar scenario from occurring with underwater drones. While UAV technologies come with certain benefits to state military forces, such as surgical precision airstrikes, their indiscriminate use by non-state actors and terrorist groups has wrought havoc across the Middle East. Preventing a similar outcome with the continued proliferation of UUVs is vital to the security of the global ocean and the ships upon it. This will require concerted efforts and significant international cooperation from governments, international organizations, and civil society groups alike. While the successful control of UUV proliferation is not impossible, states must also prepare for the adverse outcome and develop effective and efficient counter-UUV strategies and technologies.

Andro Mathewson is a Research Fellow at the Arctic Institute, a Capability Support Officer at the HALO Trust, and an International Relations MSc student at the University of Edinburgh. His dissertation explores the proliferation of uninhabited underwater vehicles (UUVs) on a global scale. He is interested in international security, military technologies, and naval warfare. Andro has previously contributed to the Bulletin of Atomic Scientists, the Texas National Security Review, the Wavell Room, and the UK Defence Journal. Before his current studies, he was a research fellow at Perry World House at the University of Pennsylvania, where he also received his Bachelor of Arts in PPE and German. The views expressed in this article are those of the author and do not necessarily reflect the official position of The HALO Trust.


1. For the purposes of this article, the term uninhabited underwater vehicles (UUV) will be used throughout. There is no generally accepted nomenclature, thus “UUV” in this paper will encompass all types of uninhabited underwater vehicles, regardless if armed, unarmed, military, civilian, autonomous, or remotely operated. UUVs are also known as underwater drones or undermanned underwater vehicles and include autonomous underwater vehicles (AUVs), remotely operated underwater vehicles (ROUVs), and underwater gliders. However, it is also important to note that this essay focusses exclusively on government owned UUVs.

2. The map illustrates states and their militaries that are in possession of UUVs, regardless if those are armed or not, or how they were acquired (developed, bought, co-owned, transferred, or captured).

3. Part of this is driven by their dual-use nature and multifaceted abilities, including, for example, wreck salvage and environmental survey, as well as by the growing number of deep-water offshore oil & gas production activities and increasing maritime security threats.

4. This data is based on an original cross-sectional database produced in May 2021, containing information on the UUV capabilities of 196 states and 2 non-state actors. I use the term “at-least” for two reasons: (1) Due to the military nature of UUVs, it is safe to assume that there is significant information pertaining to their proliferation that is publicly unavailable, and (2) despite extensive research, there is always the possibility that there are lapses in my data.

5. To analyse this data, I use a probit regression model, focusing on two dependant variables (government UUV ownership and domestic production capacity) and the following independent variables: Access to the global ocean; Ratification of the United Nationals Convention on the Laws of the Sea; Submarine ownership; UAV ownership; NATO membership; Ongoing Maritime Disputes; Military Expenditure; and GDP per capita. This model shows an estimated probability that a state with a set of particular characteristics (the independent variables) will either own UUVs or have the domestic capacity to produce them. Based on this model, the list shows states most likely to acquire UUVs next, compared to the overall characteristics of states already owning UUVs.

Featured Image: Unmanned underwater vehicles, assigned to Commander, Task Group 56.1, are pre-staged before UUV buoyancy testing. (U.S. Navy photo by Mass Communication Specialist 1st Class Julian Olivari/Released)

Cognitive Lasers: Combining Artificial Intelligence with Laser Weapon Systems

Emerging Technologies Topic Week

By Dr. Bonnie Johnson

The Advent of Laser Weapon Systems Presents a Highly Complex Decision Space 

The Navy is advancing rapidly with the development and integration of high energy laser (HEL) weapon systems onto ships to support the ship self-defense mission. HEL systems offer novel hard-kill and soft-kill engagement options with targeting accuracy and narrowly focused speed-of-light lasing with a relatively low cost per shot. HEL hard-kill engagements provide a more traditional weapon function of burning through the target to cause enough damage to render the threat useless. HEL soft-kill engagements offer “softer” options of blinding threat sensors and optics, rather than complete destruction.

HEL systems differ significantly from traditional kinetic shipboard weapon systems. Laser weapons concentrate a very highly focused beam of coherent energy on targets at a distance. They must have line of sight with the threat target. Although the laser beam travels at the speed of light, the beam must “dwell” on the target for a period of time long enough to induce soft or hard kill effects. Environmental and atmospheric effects can greatly affect laser beams, diminishing the amount of irradiance that makes it to the threat. Laser weapons require significant amounts of power, and when facing threat situations that require longer dwell times or multiple engagements, operators may need to make sure that sufficient power is available.

Figure 1 – Laser Weapon Factors of Complexity. Click to expand.

Operating laser weapons is a complex endeavor. Figure 1 identifies the many characteristics of HEL operations that lead to complexity in this decision space. At the outset, tactical operations for defensive missions have inherent complexity: threats are often unexpected and offer a very limited reaction time, situational awareness is often incomplete and uncertain, the environment is dynamic and changing rapidly, human operators can become overwhelmed with information, uncertainty, and decision options, and the consequences can be dire.

Laser weapon systems contribute additional complexity to the operator’s decision space. The operator must weigh many factors within the dynamic threat situation to choose a soft-kill or hard-kill option, select an effective target aimpoint, calculate the required laser power-in-the-bucket (amount of actual laser irradiance per area that makes it to the target) and calculate the required dwell time. The operator must consider environmental effects and must determine if enough power is available to support the engagement. The operator may also decide to use an existing kinetic weapon system instead of a HEL system depending on a comparative prediction of kill success.

During combat operations, a ship’s warfare operators will make critical kill chain (weapon engagement) decisions under highly time-critical and uncertain conditions. Figure 2 illustrates an example of a ship’s tactical operations picture in a situation involving UAV threats. In this scenario, the operators must weigh what is known about the threat with what the ship’s defensive weapon systems are capable of. In this example, the operators must predict and compare how successful the Sea Sparrow, the laser weapon system (LaWS), and the Phalanx CIWS will be against the threat UAVs. The threat’s proximity and incoming speed will dictate how much time the operators have to make these comparative predictions. In many cases, the human operators may be well-served with an automated decision support system that can quickly calculate preferred weapon options based on the situation, such as doctrine statements. The emerging capabilities of artificial intelligence can be leveraged to enable automated decision aids for laser weapons—thus creating a cognitive laser approach for laser weapon systems.

Figure 2 – Complex Decisions for Naval Weapons Operator. Click to expand. (Source: Blickley et al, 2021)

Combining Emerging Technologies: Laser Weapons and Artificial Intelligence

Two emerging technologies lead to the cognitive laser concept: laser weapon technology and artificial intelligence. The Navy has been researching laser technologies for decades and lasers have recently matured to the point where they are being integrated and tested on ships for operational use. In parallel with this evolution, there have been significant advances in artificial intelligence (AI)—particularly in the development of intelligent computer systems that can support complex decision-making. The marriage of these two emerging technologies is the genesis of the proposed cognitive laser concept. 

Laser weapon systems and their use in the defense of naval ships presents a complex decision space for human tactical warfare operators that requires the assistance of AI to process, fuse, and make sense of large amounts of data and information in short timeframes, and to develop and evaluate effective courses of action involving complex systems (including laser weapons). The laser weapon kill chain requires the intuitive, adaptive, and creative cognitive skills of humans as well as the abilities of automated systems to rapidly fuse large amounts of disparate data, construct and assess vast permutations of options, predict performance, and deal with uncertainty. Automation, artificial intelligence, and machine learning can provide a human-machine teaming cognitive solution.

November 26, 2014 — Chief of Naval Operations (CNO) Adm. Jonathan Greenert gets a firsthand look at the directed energy Laser Weapon System (LaWS) operator’s console aboard the interim afloat forward staging base USS Ponce (AFSB(I) 15) (U.S. Navy photo by Chief Mass Communication Specialist Peter D. Lawlor/Released)

Cognitive Laser Concept

Graduate students at the Naval Postgraduate School (NPS) have been studying various aspects of the cognitive laser concept. A systems engineering capstone team developed Figures 3 and 4 as they developed a conceptual design of an automated decision aid to support laser weapon engagement decisions for a naval shipboard HEL system (Blickley et al, 2021). Figure 3 presents a context diagram illustrating how the decision aid might retrieve threat information and laser resource information from onboard sensors and weapons scheduling in order to develop engagement recommendations and provide these to HEL operators.

Figure 3 – Cognitive Laser Context Diagram. Click to expand. (Source: Blickley et al, 2021)

The capstone team performed a functional analysis of the conceptual cognitive laser decision aid. Figure 4 contains a functional flow diagram from this analysis. It highlights some of the decision factors involved in determining whether or not to fire an HEL system: if there is sufficient time, if atmospheric conditions are favorable, if there is sufficient power, if the threat’s material composition can be effectively lased, and if there are no deconfliction issues (if there is no risk of friendly fire in the path of the laser beam).

Figure 4 – Cognitive Laser – Flow Diagram. Click to expand. (Source: Blickley et al, 2021)

NPS SE thesis students are studying other aspects of the cognitive laser concept. One study is widening the scope of the problem beyond laser weapon system decisions (Carr 2021). This study is asking the broader question: how do warfare operators on ships make the determination of which weapon to select when they have kinetic weapons and laser weapons to choose from? For this higher-level kill chain function, the operator needs to be able to compare the predicted performance of the kinetic weapon with that of the laser weapon for a given threat scenario. The threat is not stationary—as it moves, the range between the weapon and target changes and therefore the amount of “atmosphere” that the laser beam must traverse changes. Real-time changes in the threat’s proximity and kinematics continuously affect the projected performance of the two types of weapon systems differently. Weapon operators will be more familiar with when and how to engage a dynamic threat with kinetic weapons. They may be less familiar with the intricacies of engaging a dynamic threat with a laser weapon. The required laser’s dwell time and power needs will change as the threat moves and maneuvers. The complexities of a projected performance prediction between the two different types of weapons warrants the use of AI and automated decision aids to support this complex decision space.

As threats advance in complexity, naval operators will need to use laser weapon systems in more sophisticated and complex operations. NPS is studying the use of laser weapons to defend against future swarms of drones (Taylor 2021). The study is first characterizing possible drone swarms—their configuration, the number of drones, and the types of drones. The study is exploring the capabilities of laser weapons to address the swarms—soft-kills, hard-kills, and engagement timelines to understand how many drones can be addressed in a given situation. The study is developing strategies to apply different engagement logic to different threat scenarios—a series of soft-kills, or strategic hard-kills, or combinations of lasing and using kinetic weapons, as examples. The rapid development of effective laser weapon engagement logic in such complex tactical situations will require a cognitive laser approach to aid laser operators.

May 16, 2020 — USS Portland (LPD-27) successfully disables an unmanned aerial vehicle (UAV) with a Solid State Laser. (Video via USNI News)

Tactical energy management, as illustrated in Figure 5, is a cognitive laser concept for allowing laser weapon operators to understand and manage the dynamic energy resources during tactical operations. Laser weapons require significant amounts of energy when they are fired, and energy is a constrained resource on ships. This concept taps into the power sources on a ship to give laser operators insight into how much power is available and to determine how much power will be required to defeat specific threats as they are encountered.

Figure 5 – Tactical Energy Management. Click to expand. (Source: Armentrout et al, 2017)

Machine learning is an AI method that involves computers “learning” effective solutions or answers by training them using great amounts of data or scenarios. Recent research projects at NPS have been studying the use of machine learning approaches for determining the required dwell time based on the properties of the material composition of targets (Blickley et al 2021) and for target selection and engagement strategies against drone swarm threats (Edwards 2021). From the operator’s perspective, a machine learning algorithm would enhance a real-time decision aid by providing an expert-level laser weapon system knowledge base as shown in Figure 6. As real-time sensor data provides information about the threat—its location (or locations for a swarm threat), kinematics, and characteristics, the decision aid can assess and predict the target type, location of components (fuselage, sensors, seekers, etc.), material composition and thickness. This information is compared with the machine learning knowledge base which produces accurate recommendations for engagement strategy, aimpoint selection, and laser dwell time. 

Figure 6 – Machine Learning for the Cognitive Laser (Source: Blickley et al, 2021)

Laser weapon operations pose a friendly fire risk. Lethal laser beams can unintentionally harm nearby friendly forces (aircraft, ships, etc.) or civilian entities in the vicinity. Deconfliction planning is a critical function in the laser weapon kill chain to ensure that the “coast is clear” so that the path of the laser beam is free of friendly and civilian assets. NPS studies are developing concepts for ensuring and managing deconfliction for different military laser weapon applications (Kee et al. 2020, Clayton et al. 2021). In time-critical tactical operations, laser weapon operations will require a cognitive laser approach to ensure for proper deconfliction.

The realization of a cognitive laser requires advances in human-machine teaming research to ensure the effective and safe employment of AI methods. Several studies at NPS are researching different aspects of applying AI to the tactical domain. Jones et al (2020) studied the air and missile defense kill chain to show that human-machine teaming arrangements can adapt in response to the threat situation timeline. The threat will dictate how much time the operator has to react, and this can be incorporated into the design of AI-enabled automated decision aids. Burns et al (2021) are embarking on a research project to map specific AI methods to the specific functions of the kill chain. Tactical kill chains (including laser weapon kill chains) require a variety of cognitive skills and decisions. These include data fusion, assessment, knowledge discovery, addressing uncertainty, developing course-of-action alternatives, predicting system performance, weighing risks, and gaming second- and third-order strategies.

A wide variety of AI methods will be needed to support these kill chain functions. Cruz et al (2021) are studying the potential safety risks and failure modes that may be introduced as AI and automation is adopted in the tactical domain. Safety risks may be inherent to the AI systems and their decision recommendations, or they may come in the form of cyber vulnerabilities as AI is introduced into tactical systems, or they may arise from the interactions of humans with intelligent machines. Peh (2021) is taking a deep dive into the complex dynamics of trust between humans and AI systems by researching methods to engineer AI systems for tactical operations. Peh’s research mission is to engineer AI systems as tactical decision aids that are trustworthy and achieve an effective trust balance to avoid both over-trust (humans blindly trusting AI) and under-reliance (humans disregarding AI).  


Two emerging technologies are pairing up to provide new capabilities for the warfighter of the future: laser weapons and AI. Laser weapons are becoming an operational reality for defending ships and fleets, but they also pose an operational challenge in the form of decision complexity. AI is the necessary companion that can tackle this decision complexity and support effective human-machine teaming to operate laser weapons effectively and safely. A cognitive laser solution marries these two emerging technologies. The cognitive laser concept opens a diverse and challenging field of research for innovations in the application of AI methods to both laser weapon operations and the military tactical domain in general.

Dr. Bonnie Johnson is a senior lecturer of systems engineering at the Naval Postgraduate School. She was previously a senior systems engineer in the defense industry from 1995–2011 working on naval and joint air and missile defense systems. A graduate of Virginia Tech with a bachelor of science in physics and a graduate of Johns Hopkins with a master of science degree in systems engineering, Dr. Johnson received her PhD in systems engineering from the Naval Postgraduate School.


Armentrout, A., Behre, C., Ngo, T., Rowney, D., Schroder, E., and Stopper M., 2017. “Objective architecture for tactical energy management of directed energy weapons,” Naval Postgraduate School Capstone Report, March 2017.

Blickley, W., Carlson, J., Magana, M., Pacheco, A., and Roscher J., 2021. “Cognitive laser – automated decision aid for a system of laser weapon systems,” Naval Postgraduate School Capstone Report, March 2021.

Burns, G., Collier, T., Cornish, R., Curley, K., Freeman, A., and Spears, J., 2021. “Evaluating artificial intelligence methods for use in kill chain functions,” Naval Postgraduate School Capstone Proposal, April 2021.

Carr, A. 2020. “A proposed model for a shipboard high energy laser and kinetic weapons system automated decision aid,” Naval Postgraduate School Thesis Proposal, October 2020.

Clayton, B., Scott, M., Shelton, J., Williamson, J., and Vermillion, M., 2021. “Highway to HEL – USMC expeditionary employment of a high energy laser to counter drone threats,” Naval Postgraduate School Capstone Proposal, July 2021.

Cruz, L., Hoopes, A., Pappa, R., Shilt, S., and Wuornos, S., 2021. “Evaluation of the safety risks of developing and implementing automated battle management aids for air and missile defense,” Naval Postgraduate School Capstone proposal, May 2021.

Edwards, D. 2020. “Application of machine learning for a laser weapon system aimpoint selection decision aid in support of a cognitive laser.” Naval Postgraduate School Thesis Proposal, August 2020.

Jones, J., Kress, R., Newmeyer, W., and Rahman, A., 2020. “Leveraging artificial intelligence for air and missile defense: an outcome-oriented decision aid,” Naval Postgraduate School Capstone Report, September 2020.

Kee, R., Lutz, T., Schwitzing, M., Murray, E., 2020. “Impact on shipboard power generation and storage when utilizing high energy laser systems to counter anti-ship cruise missiles in fleet defense scenarios,” Naval Postgraduate School Capstone Report, September 2020.

Peh, M., 2021. “Developing a trust metric in engineering an artificial intelligence enabled air and mission defense system,” Naval Postgraduate School Thesis Proposal, November 2020.

Taylor, A. 2021. “Shipboard laser weapon system automated decision aid: countering unmanned aerial vehicle swarm threats,” Naval Postgraduate School Thesis Proposal, January 2021.

Featured Image: Dahlgren, VA – ARABIAN GULF (Nov. 16, 2014) The Afloat Forward Staging Base (Interim) USS Ponce (ASB(I) 15) conducts an operational demonstration of the Office of Naval Research (ONR)-sponsored Laser Weapon System (LaWS) while deployed to the Arabian Gulf. (U.S. Navy photo by John F. Williams/Released)

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,

4. Denning, P., Lewis, T. Intelligence May Not be Computable. American Scientist. Nov-Dec 2019.

5. Hao, K. (2021, April 1) Error-riddled data sets are warping our sense of how good AI really is. MIT Technology Review.

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.

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,

8. Office of Naval Research, Automated Offboard Refueling and Data Transfer for Unmanned Surface Vehicles, BAA Announcement # N00014-16-S-BA09,

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)

Red Cell Analysis of a Mobile Networked Control System Supporting a Ground Force

Emerging Technologies Topic Week

By Larry Wigington, Ruriko Yoshida, and Doug Horner

The U.S. Department of Defense (DoD) will increasingly employ unmanned, autonomous systems to support ground force operations.1 However, if an opposing force can track these unmanned systems, or identify patters in the systems’ behavior, it may be able to determine the location and intended movements of the ground force. In this article, we present an adversarial, or “red cell,” analysis of prior Naval Postgraduate School (NPS) work with an Unmanned Vehicle (UxV) Networked Control System (NCS) to determine if multiple, distributed, heterogeneous unmanned systems behave in a manner that may compromise the operations security (OPSEC) of a ground force.

A UxV NCS consists of heterogeneous agents, which include unmanned and manned systems, where each node has communications, sensing, and mobility capabilities and constraints. Research conducted at NPS by Lowry2 and Wachlin3 focused on a centralized and distributed adaptive submodular optimization approach to find near-optimal, near-real-time, position solutions for agents in an NCS. Their work has produced methodologies that can find near-optimal position recommendations—which optimize over system parameters, such as sensing and communication—to increase mission effectiveness. However, over time these recommendations can create predictable paths that may reveal the location and intended movements of a ground force.

Determining if this is the case requires two assumptions; paramount is the ability for the adversary to observe and collect position data about the UxV NCS. Our red cell analysis is based on a “worst-case scenario,” using data from a real-world experiment in which a network of autonomous vehicles supported a ground force. In this step, we assume the adversary has perfect information about previous force disposition, system configurations, and access to detailed training data, including time-stamped geolocation information that links the ground force to a UxV NCS. Our second assumption is that opposing forces will use Artificial Intelligence and Machine Learning Techniques to identify patterns in the unmanned systems’ behavior.

The Scenario

In November of 2017, NPS designed and conducted a Multi-Thread Experiment (MTX) series on San Clemente Island, which brought together faculty and students from across NPS, including the Center for Autonomous Vehicle Research (CAVR), Center for Network Innovation and Experimentation (CENETIX), Consortium for Robotics and Unmanned Systems Education and Research (CRUSER), and Joint Interagency Field Experimentation Program (JIFX), as well as fleet sponsors such as Naval Special Warfare (NSW), Commander, U.S. Third Fleet (COMTHIRDFLT), and Naval Information Warfare Systems Command (NIWC).

The experiment provided a platform to test the collaboration of a UxV NCS with manned assets (Figure 1). The scenario was built around an NSW team tasked with landing on San Clemente Island to conduct a direct action mission on a known target. The NSW team was supported by the autonomous systems to provide Intelligence, Surveillance, andReconnaissance (ISR) support, transportation, and battle-space awareness. The MTX generated a reliable dataset to test the hypothesis that an NCS generates predictable paths that could compromise the OPSEC of a ground force. Additionally, the scenario provides the opportunity to conduct a more robust analysis, as the NCS dynamically positioned itself to support the ground force.

Figure 1: Two dimensional illustration of one scene from a Multi-Thread Experiment (MTX) series on San Clemente Island in November 2017, which was designed and conducted by NPS.

The Analysis

Our analysis of the NCS utilized machine learning techniques to predict each autonomous vehicle’s path and the ground force’s path and target during the MTX. We further developed and applied a novel framework to automate the analysis of the NCS-generated data from all aspects of data science, including supervised and unsupervised learning. This framework establishes a foundation to automate future red cell analyses of NCS data, which could be later incorporated into the NCS methodology to improve the OPSEC of supported ground forces. 

The framework consists of two independent models and a hierarchical model. The UxV Forecast Model conducts univariate time series analysis of the x- and y- coordinates, where the x and y values are generated for a localized grid coordinate system in a Northing Easting Down (NED) configuration and the point (0, 0) is based at lat-33.03191N, lon-118.60428W. This model builds twelve independent models and then forecasts each model a certain number of steps into the future. The output of the UxV Forecast Model creates the predicted locations of all NSC components.

The Ground Force Triangulation (GFT) model uses numeric regression techniques to calculate the ground force’s location as a function of UxV positions. The trained GFT model is then used in the time series regression model; the outputs of the GFT model are compared to the validation data to determine if the model is sufficient. The time series regression model combines the predictions from the UxV forecast model with the optimized model from the GFT model to generate the ground force’s predicted locations, which can then be compared against their actual positions.

The Results

The Unseen Data Set (UDS) was obtained from the research conducted by Lowry with a distributed submodularity. Lowry modified the data set, which we used for training our models to create a new ground force path, objective, and simulated UxV positions. We used the UDS to conduct our analysis from the perspective of an adversary that is observing autonomous vehicles and a U.S. Navy warship off its coast. The adversary previously obtained the MTX data sets and has two models which may be able to predict a ground force’s path, if one is present.

Figure 2: Time Series Neural Network Regression Model Results – Unseen Data Set. Left: UAV actual paths and predicted paths via time series models. Right: The NSW actual and predicted paths via the triangulation model. Click to expand.

The right-hand image of Figure 2 compares the NSW team’s predicted and actual paths. What is most evident in this image is that the predicted path and the actual path are nearly identical. Over the 24-minute prediction horizon, the mean path deviation is only 39 meters; what is more impressive is that the largest path deviation occurs in the middle of the prediction space and is only 75 meters. The predicted NSW locations that occur after this mid-point converge back towards the NSW team’s actual path.


Our analytical models correctly identified the ground force’s intended movements in both scenarios. The ground force’s predicted path deviated from the actual path by an average of only 39 meters. The implications of these results are far-reaching as DoD begins to focus on competing with near-peer adversaries in the Indo-Pacific Theater, and the Marine Corps identifies the need for reconnaissance and counter-reconnaissance capabilities when conducting operations within the “weapons engagement zone.” Future analyses of this kind will be a valuable component in planning and executing missions involving ground forces supported by mobile networked control systems.

Maj. Larry Wigington is an operations research analyst for U.S. Marine Corps’ Manpower and Reserve Affairs. He previously served as Adjutant for the 26th Marine Expeditionary Unit and G-1 Operations Officer for 2d Marine Division. He earned a master of systems analysis and master of science in operations analysis at the Naval Postgraduate School, and a bachelor of science in criminal justice at Troy University.

Dr. Ruriko Yoshida is an associate professor of operations research at the Naval Postgraduate School. She was previously an assistant and then associate professor at the University of Kentucky from 2006-2016, and a postdoctoral researcher at Duke University and the University of California, Berkeley, from 2004-2006. Dr. Yoshida received her PhD (2004) in mathematics from the University of California, Davis. 

Dr. Douglas Horner is a research assistant professor in the Department of Mechanical and Aerospace Engineering at the Naval Postgraduate School (NPS). He was a previously a naval special warfare officer (SEAL) from 1989-2001. A graduate of Boston University with a bachelor of arts in math and economics, Dr. Horner earned his PhD in computer science, master of science in applied mathematics, and master of arts in national security affairs at NPS. 


[1] Defense Science Board. The role of autonomy in DoD systems. Technical report, Defense Science Board, Washington, DC, 2012.

[2] Bryan Lowry. Distributed submodular optimization for a uxv networked control system. Master’s thesis, Department of Mechanical and Aerospace Engineering, Naval Postgraduate School, Monterey, CA, 2020.

[3] Noah Wachlin. Robust time-varying formation control with adaptive sub-modulairty. Master’s thesis, Department of Mechanical and Aerospace Engineering, Naval Postgraduate School, Monterey, CA, 2018.

Featured image: April 29, 2018 – U.S. Marine Corps 2nd Lt. Michael Francica, with Combat Logistics Battalion 8, Combat Logistics Regiment 2, 2nd Marine Logistics Group, pilots an InstantEye quadcopter during an operations check for Marine Corps Warfighting Laboratory as part of Integrated Training Exercise 3-18 on Marine Corps Air Ground Combat Center, Twentynine Palms, Calif. (Credit: U.S. Marine Corps)