United States

Knowledge of underwater acoustics in diverse oceanic environments is key to accurate analysis of sonar systems used for Air Anti-Submarine Warfare (ASW) simulations. By identifying variations in underwater sound propagation, analysts score the acoustic abilities of sonar tools according to their performance in distinct regions. Since these variations are sensitive to environmental changes, leveraging known underwater properties to group oceanic regions could be used to improve acoustic analysis and inform decision makers.

NAML 2025

Ocean Provincing Tool

air anti-submarine warfare

air asw

undersea

acoustics

clustering

### Welcome to the 9th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
<br>Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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* To spread awareness of current machine learning research relevant to Naval applications
* To connect machine learning researchers with experts in military requirements
* To build and strengthen collaborations within the defense research community

#### *"What we do as a Navy is a human business. Don’t pass up an opportunity to come together as a team. Don’t pass up leadership opportunities, and don’t outsource them to a PowerPoint. If you have an opportunity to come together as a team, to increase belonging, do it."*
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The 9th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.

technical paper

This talk is going to discuss recent advances and next steps for assured spectrum perception using AI/ML techniques in tactical environments.

Resilient Dynamic AI/ML for Assured Spectrum Perception in Tactical Environments: Recent Advances, Existing Challenges and Next Steps

Radar-based automatic target recognition (ATR) of UAVs is critical for security and defense. UAV recognition is commonly done using micro-Doppler (μD) signatures which are characterized by Helicopter Motor Rotation (HERM) lines that appear parallel to the main UAV return and are unique per the number of and rotation rate of propellers. This paper addresses two challenges: (1) accurate synthesis of target signatures and (2) development of classifiers robust to dynamic clutter environments, which have a different distribution than the training dataset. 

The first challenge to deep learning-based ATR is a lack of adequate amounts of quality data for training. It is often not feasible to acquire data in operational areas of interest, consequently synthetic training data generation has been a topic of intense research. However, current methods do not account for sensor-specific, target-specific, or environment-specific characteristics, which may also vary with time. Generative Adversarial Networks (GANs) are an alternative way to synthesize data from a few samples of real data, which can capture such dynamic factors. However, our prior work has shown that GANs synthesize kinematically impossible target signatures, which can degrade classification accuracy. In synthetic UAV signatures, HERM lines are often distorted, duplicated, or the trajectories are corrupted. The second challenge is that data acquired in real operational environments often has a signal-to-interference-plus-noise ratio (SINR) than the training dataset which degrades target recognition.

We propose a novel physics-based approach that addresses both UAV signature synthesis and classification in dynamic environments. First, we develop a Physics-Guided Semantic Segmentation (PhG-SS) network which uses U-Net as a backbone and is trained on low-fidelity physics-based UAV simulations. To then synthesize large datasets for training which account for the inherent kinematics of UAVs, we propose a Semantic-Segmentation GAN (SS-GAN) in which PhG-SS is added as an auxiliary discriminator branch. We show that this results in synthesis of UAV signatures with kinematically accurate HERM lines. When SS-GAN data is used in training, we found that it improved the classification accuracy of a convolutional neural network (CNN) even when there is mismatch between the SINR of training and test sets. Second, we propose a Semantic-Segmentation DNN (SS-DNN) in which PhG-SS is integrated as signal conditioning prior to convolutional layers. Our results show that the combination of training the SS-DNN using SS-GAN signatures synthesized from high SINR data results in the highest classification accuracy when testing on UAV signatures with low SINR. The implications of these results are that our proposed approach can enable target recognition in new environments from which no a priori training data is available.


Physics-Guided Semantic Segmentation for UAV Radar Micro-Doppler Synthesis and Classification in New Environments

Point measurements of optical turbulence often rely on sensitive instruments that can be challenging to deploy in various environments, and existing turbulence models may not always perform well under these conditions. In a recent experiment at the Naval Postgraduate School, optical turbulence measurements were collected using a sonic anemometer along with various atmospheric parameters (e.g., air temperature, humidity, solar flux) over a thirteen-month period. Our previous work utilized these atmospheric parameters as predictors to develop a bagged ensemble of regression trees, with optical turbulence as the response variable, and employed feature selection techniques to identify the most relevant parameter set. Building on this foundation, we conducted an in-depth analysis of how varying random partition sizes affect the model’s RMSE. Overall, the predicted values from the model demonstrated strong agreement with the observed turbulence values derived from the sonic anemometer, highlighting the model's robustness and practical applicability.

Enhancing Machine Learning Prediction Models of Optical Turbulance Through Feature Selection and Partition Analysis

Station-Keeping short-duration high-altitude balloons (HABs) in a region of interest is a challenging path-planning problem due to partially observable, complex, and dynamic wind flows. Deep reinforcement learning is a popular strategy for solving the station-keeping problem. A custom simulation environment was developed to train and evaluate Deep Q-Learning (DQN) for short-duration HAB agents in the simulation. To train the agents on realistic winds, synthetic wind forecasts were generated from aggregated historical radiosonde data to apply horizontal kinematics to simulated agents. The synthetic forecasts were closely correlated with ECWMF ERA5 Reanalysis forecasts, providing a realistic simulated wind field and seasonal and altitudinal variances between the wind models. DQN HAB agents were then trained and evaluated across different seasonal months. To highlight differences and trends in months with vastly different wind fields, a Forecast Score algorithm was introduced to independently classify forecasts based on wind diversity, and trends between station-keeping success and the Forecast Score were evaluated across all seasons.

Seasonal Station Keeping of Short-Duration HABs using Deep Reinforcement Learning (DRL)

Integrating advanced machine learning paradigms with autonomous systems has opened new frontiers in robotics. Inspired by Hapi, the Egyptian god of coordination and harmony, this project introduces HAPI-RL. This framework merges voice-command-driven operations with reinforcement learning (RL) to enable real-time, adaptive drone swarm control. By combining natural language processing (NLP) and RL, HAPI-RL transforms voice inputs into coordinated drone actions, paving the way for innovative disaster response, logistics, and surveillance applications.
At the heart of HAPI-RL is a custom-designed RL pipeline tailored to control a drone swarm operating on the ArduPilot platform. The pipeline integrates state-of-the-art RL algorithms such as Proximal Policy Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG) to train autonomous agents capable of interpreting and executing complex voice commands. Multi-agent RL frameworks like RLlib are employed for swarm coordination, enabling drones to maintain formation, avoid collisions, and optimize task allocation dynamically. This approach ensures seamless execution of coordinated tasks while adapting to dynamic environmental changes.
Voice commands are captured and processed using open-source voice-to-text frameworks like Vosk, Kaldi, and Mozilla DeepSpeech, forming the backbone of the command recognition pipeline. Preprocessing pipelines for audio signals ensure synchronization with telemetry data, allowing accurate mapping of voice inputs to actions. To enhance reliability in high-noise environments, robust signal processing algorithms are implemented, ensuring precision in command recognition.
HAPI-RL leverages simulation environments like Gazebo and Webots to train RL models in diverse scenarios. These simulations represent the drone swarm's behavior in various operating conditions. Additionally, Software-In-The-Loop (SITL) frameworks validate behaviors before deployment in real-world settings. Upon successful training, the models are deployed on edge devices such as Raspberry Pi or Nvidia Jetson to achieve low-latency, real-time decision-making. MAVLink-based integration ensures seamless communication between the edge devices and drones.
A key feature of HAPI-RL is the implementation of abort commands as high-priority interrupts, enabling immediate override of ongoing tasks in critical scenarios. This guarantees safety and operational reliability, especially in high-stakes missions. Furthermore, extensive testing under noisy and dynamic environments validates the system's robustness, pushing the boundaries of autonomous swarm control.
HAPI-RL offers a scalable, adaptive solution for autonomous drone swarms by combining voice-to-text NLP with RL-driven coordination. Its applications range from disaster relief and surveillance to precision logistics, marking a significant advancement in autonomous systems. Through its innovative design and integration of cutting-edge technologies, HAPI-RL not only honors its namesake by fostering harmony and coordination but also sets a new benchmark for intelligent swarm control.



HAPI-RL: Hierarchical Autonomous Processing and Intelligence with Reinforcement Learning for Drone Swarms

Project Nereus explores the integration of OpenAI Swarm and agentic AI to coordinate Autonomous Underwater Vehicles (AUVs) for enhanced undersea operations, particularly in the context of Subsea and Seabed Warfare (SSW). The system aims to enable AUVs to collaborate and share information effectively, leading to improved efficiency in tasks such as exploration, mapping, anomaly detection, and underwater target threat detection, directly supporting the need for maintaining US overmatch in the undersea domain. This approach addresses challenges such as limited communication, unpredictable conditions, and real-time adaptation needs in underwater environments. The project will involve designing AUV agents with functionalities for movement, sensor data acquisition, communication, and task execution. It will also incorporate a realistic simulation environment for testing and refining the system, leveraging tools like Gazebo or UUV Simulator. The project aims to demonstrate the feasibility of using OpenAI Swarm for undersea operations, develop and test AI agent behaviors, and create a realistic simulation environment for evaluating system performance. Ultimately, Nereus aims to provide an integrated solution that can be deployed on unmanned systems to support naval undersea warfare operations, directly contributing to the goals outlined for SSW.

Project Nereus: OpenAI Swarm for Undersea Autonomous Operations

Passive underwater acoustic recording technologies are essential for identifying and localizing underwater objects. However, advanced analyses of this high-volume data type are typically performed in post-processing or aboard vessels with abundant power. With the rise of autonomous maritime vehicles and underwater robotics, passive acoustic sensing is increasingly viewed as an attractive solution for environmental and situational awareness. Utilizing this data on resource-constrained edge devices, however, requires efficient real-time processing with low power consumption. Traditional multi-target tracking methods often rely on computationally intensive techniques and still frequently require manual intervention to clean and identify tracks. To address the growing need for real-time acoustic data processing, we introduce an automated multi-target tracking system designed for small volumetric four-channel hydrophone arrays. Our approach is optimized for low latency (~1.7 ms) and low power consumption (~1.3 W) on a Raspberry Pi Zero 2W. We demonstrate the system’s performance on passive acoustic recordings of diving beaked whale groups in open ocean conditions. Our method integrates onboard neural networks with post-training optimization to enhance tracking performance while minimizing computational demands. This lightweight processing system is designed for cross-platform flexibility, making it suitable for deployment on gliders, drifters, and profilers. We detail the methodology and present preliminary results, highlighting its potential for real-time autonomous underwater applications.

Listening at the Edge: Real-time Multi-Target Acoustic Tracking for Mobile Assets

Machine learning (ML)-based sonar perception is most commonly developed through the lens of offline image processing, i.e., measured sonar time series are converted into spatial acoustic intensity maps (2D or 3D) and processed, one map at a time, using neural networks with convolutional filters. While this is sufficient for many scene classification, reconstruction, and segmentation tasks, real-time 3D autonomy tasks, such as dynamic harbor navigation, are understood to benefit from both temporal memory and multi-sensor perception, due to the inherent ambiguities in sonar, the possibility of occluded agents, the 3D nature of underwater navigation, etc.

A multi-modal, self-supervised perception algorithm was designed to provide spatio-temporal awareness information to an autonomy system. The algorithm has (a) the ability to model temporal dependencies (i.e., analyze new data in the context of its previously measured surroundings and the anticipated context in the near-future), and (b) a fused, synchronized, multi-sensor perception schema, and (c) was trained in a completely self-supervised, online manner. The initial prototype was trained on Forward-Look Sonar (FLS) using simulated data from HoloOcean. The objective is to build a rich feature space that can be used to produce both interpretable outputs and also support higher-level autonomy, such as route planning, target identification,
and so on.

Multisensor Spatiotemporal Awareness

Digital twins provide real-time, high-fidelity predictions that are essential for fatigue
analysis, assessing the integrity of components under extreme loading, and designin
resilient materials for aerospace, automotive, and civil infrastructure. Accurate modeling of material response to deformation is critical to ensure safety and performance across industries. For this purpose, machine learning (ML) has emerged as a promising alternative to physics-based simulations, which often rely on constitutive relations that do not fully capture all processes present in real-world scenarios. However, ML models lacking  physically principled design struggle to generalize effectively, particularly in scenarios involving complex strain histories and multiaxial loading conditions.

This work demonstrates the advantage of enforcing material objectivity, a fundamental physical constraint that governs how the stress tensor transforms under rotations and reflections. We compare a standard convolutional neural network to an equivariant convolutional neural network, which exactly respects O(3) symmetry at the architectural level. Both models are trained on the strain to stress relationship of J2 plasticity; however, only the equivariant model is guaranteed to learn mappings consistent with the principle of material objectivity. As a result, our equivariant model achieves superior prediction accuracy across complex deformation scenarios.

Learning Metal Plasticity with an Equivariant Neural Network

The cornerstone of this effort is the integration of taxonomies and ontologies to enable semantic enrichment and improved data interoperability. This approach will facilitate the efficient discovery of data assets, making it easier for users to connect diverse datasets, regardless of their source or structure. Specifically, the semantic enrichment of existing datasets will enable compatibility with AI tools such as Large Language Models.

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Resilient Dynamic AI/ML for Assured Spectrum Perception in Tactical Environments: Recent Advances, Existing Challenges and Next Steps

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