From detecting hidden threats to defending critical underwater infrastructure, Anti-Submarine Warfare (ASW) is a cornerstone of national security. AI and machine learning are transforming the way we navigate and overcome the challenges of the underwater domain.
Amid rising geopolitical tensions, submarines have become increasingly pivotal to defence and national security - protecting sovereign waters, safeguarding undersea infrastructure, and maintaining a credible nuclear deterrent. Although exact figures remain elusive due to the inherent secrecy of submarine warfare, it’s believed that around 150–200 submarines are actively patrolling the world’s oceans at any given time.
The other side of Anti-Submarine Warfare (ASW) is the ability to detect and identify adversary submarines quickly and accurately. Yet, the nature of the underwater battlespace makes this an immense challenge - one that demands the seamless integration of cutting-edge hardware, software, and mission planning to stay a step ahead of potential threats. Significant efforts are already underway to achieve this transformation, notably through ambitious initiatives like Atlantic Bastion.
Announced as part of the Strategic Defence Review, Atlantic Bastion envisions “a layered and integrated sensor network spanning surface, subsurface, and aerial domains, coordinated across multiple stakeholders including the Royal Air Force (RAF), UK Strategic Command, the UK Hydrographic Office, NATO, and industry partners.” The multifaceted nature of this vision reflects the complex and varied nature of ASW.
ASW relies on a range of complementary techniques to detect, track, and classify submarines.
Acoustic Detection
At the heart of ASW lies sonar technology, using acoustic waves to locate submarines. Both active and passive sonar systems are employed across a variety of platforms, including hull-mounted sensors, towed arrays, and multi-static networks.
These systems are highly effective and adaptable, capable of providing detailed acoustic insights across vast areas. However, their performance is often constrained by their range limitations and the growing sophistication of countermeasures such as acoustic decoys and advanced quieting technologies that reduce a submarine’s acoustic signature.
Surface Ship ASW
Modern navies deploy surface combatants such as destroyers, frigates, and corvettes equipped with hull-mounted sonar, towed arrays, and integrated combat systems. These vessels provide a persistent presence in operational areas, capable of both detecting and engaging underwater threats.
Airborne ASW
Complementing surface and subsurface operations, airborne platforms such as maritime patrol aircraft and helicopters play a critical role in modern ASW. Equipped with sonobuoys, dipping sonar, and Magnetic Anomaly Detectors (MAD), they offer a rapid-response capability and can cover large ocean areas quickly.
Their limitations stem from range constraints dictated by their fuel capacity and their susceptibility to adverse weather conditions. Moreover, effective engagement of detected submarines requires close coordination with surface and subsurface forces.
Submarine ASW
Submarines themselves are an integral part of ASW operations. Most are equipped with passive sonar systems that enable them to detect enemy submarines covertly without revealing their own location. Once a target is identified, they can engage directly or coordinate attacks with surface or airborne units.
Their combination of stealth, manoeuvrability, and lethality makes them indispensable assets to any modern navy.
Fixed and Distributed Sensor Networks
Another important component of ASW is the use of fixed and distributed sensor systems. These networks provide long-range detection with low maintenance demands once deployed. However, they are expensive and logistically challenging to install, while their fixed positions reduce flexibility and make them vulnerable to sabotage and environmental degradation.
Unmanned Systems
Recent advances in artificial intelligence and autonomous technologies have positioned unmanned systems, such as Unmanned Surface Vehicles (USVs) and Unmanned Underwater Vehicles (UUVs), as key assets in the modern underwater battlespace.
When equipped with sonar arrays and other sensing technologies, these platforms can extend detection coverage and operate in high-risk areas without endangering personnel. They also offer significant scalability advantages, being easier and more cost-effective to deploy in large numbers, which supports the development of networked, multi-node detection and tracking systems.
Despite these benefits, unmanned systems come with their own set of challenges. They often have limited endurance and payload capacity, and their effectiveness depends on reliable, secure communications for coordination with other assets. Additionally, they remain vulnerable to interference, jamming, and potential capture or destruction in contested environments.
Each ASW methodology offers unique advantages and constraints, but three core challenges transcend all approaches: the complexity of the underwater environment, managing the cognitive demands on operators, whether they operate on a platform or remotely, and the rapid evolution of submarine threats.
The underwater battlespace is one of the most complex and variable environments in which to operate. Sound behaves unpredictably: it varies in intensity, travels along non-linear paths, and is influenced by factors such as temperature, salinity, water currents, seabed composition, and weather conditions.
Acoustic signals also arrive from multiple directions and sources, near and far. The result is a dense and dynamic soundscape in which it can be difficult to distinguish a hostile submarine from natural or man-made background noise.
ASW operations take place in high-stakes, time-sensitive environments that demand rapid and accurate decision-making. Operators must interpret large volumes of sensor data, often presented in different formats and varying levels of relevance. This cognitive load scales with the volume of data collected. Although this can be managed to a greater or lesser degree on a single platform, with the potential for hundreds of sensors and with each sensor producing increasing amounts of data, it will nevertheless lead to a human decision-making process.
Without effective data fusion and visualisation tools, this information overload can hinder rather than help, increasing the likelihood of errors or delayed responses during critical moments. As next-generation hardware and sensors continue to generate even more data, the cognitive burden on operators will only increase unless smarter systems are introduced.
Modern submarines have become increasingly sophisticated and stealthy. Advances in design and technology, including anechoic coatings, pump-jet propulsors, and sound-isolated machinery, have dramatically reduced their acoustic signatures, making passive sonar less effective.
Submarines can also tactically exploit the environment to their advantage, operating below thermal layers where temperature gradients bend sound waves and create shadow zones that degrade sonar performance. Variations in salinity, pressure, and ambient noise from marine life and shipping further complicate detection. When combined with tactics like slow-speed manoeuvring and seabed masking, these factors make identifying and tracking modern submarines a formidable task.
Acoustic detection remains a cornerstone of ASW, but its effectiveness relies on overcoming these challenges. Recent advances in artificial intelligence (AI) are transforming this landscape, enabling forces to interpret complex acoustic environments more rapidly and effectively than ever before.
By automating analysis, AI reduces cognitive load, helps operators detect evolving threats in real time, and enhances situational awareness across multiple domains. This evolution marks the emergence of a new paradigm in underwater defence: Acoustic Intelligence (ACINT), where AI-driven insights turn acoustic data into a decisive operational advantage.
Acoustic Intelligence (ACINT) in defence involves the collection and analysis of underwater acoustic data to locate, identify, and assess threats, supporting informed strategic decision-making. The integration of AI and machine learning into operator workflows addresses the core challenges of ASW, environmental complexity, cognitive overload, and evolving threats, while AI-in-the-loop approaches ensure that human oversight and accountability remain central.
Acoustic signatures can be transformed into visual representations, such as spectrograms, enabling computer vision techniques to identify patterns and signals of interest. This allows warfare specialists to leverage their domain expertise while focusing on the most relevant data. By substantially reducing the volume of data, AI manages the cognitive load on operators, enabling them to focus on the most important problems and make faster and more accurate decisions.
Machine learning also enables continuous improvement in detection capabilities. As new submarine threats are encountered, data can be fed back into the system to refine and adapt detection models. This process, which we refer to as continuous meta-learning, ensures the system evolves alongside threats while mitigating the risks associated with misaligned AI, maintaining its effectiveness over time.
Anti-Submarine Warfare (ASW) has always been a cornerstone of national defence, but in today’s era of increasing geopolitical instability, it has become a strategic necessity. The Royal Navy has acknowledged this reality with the launch of Atlantic Bastion, recognising that effective ASW — alongside a continuous at-sea deterrent — requires the integration of multiple methodologies, sensors, and capabilities to overcome the limitations of any single approach.
While acoustic detection remains essential for identifying and tracking threats that produce minimal signatures, the true potential of Acoustic Intelligence (ACINT) lies in the AI-driven correlation of data from multiple sensor types. By combining these inputs, operators can mitigate individual limitations, exploit complementary strengths, and gain a more complete understanding of the underwater battlespace.
No single organisation has all the resources, expertise, or capabilities needed to achieve this alone. Success depends on collaboration, from central government, defence primes, and SMEs to domain experts and operational end-users in the field, to ensure that no threat goes undetected and that our oceans remain secure.
Interested in AI-enabled acoustic intelligence for ASW? Get in touch.