United States

Ever since the first introduction of computers and physics-based models, the US Navy has utilized model and simulation (M&amp;S) techniques to improve designs, develop new technologies, and increase the capabilities of the fleet. As M&amp;S techniques have evolved, so have the applications within the naval community. For example, the introduction of real-time simulations and hardware in the loop testing allowed rapid prototype development and the de-risking of shipboard power systems. Presently, innovations in artificial intelligence (AI) and machine learning (ML) algorithms offer another immense opportunity for the US Navy, as AI/ML could greatly impact how warfighters complete their missions. 
Just like any new technology however, AI/ML must be thoroughly validated before they can be fully accepted. In the case of hardware in the loop testing, decades of experiments showed that the testing of hardware in a virtual environment is just as rigorous as traditional testing methods. Unfortunately, AI/ML does not have a standard deployment method. This creates a challenge in comparing different AI/ML solutions to a problem or evaluating the performance of an agent/algorithm in different scenarios. In this work, the authors propose one potential method by demonstrating the evaluation of machine learning algorithms via software in the loop (SIL) testing, where the algorithm connects directly to a real-time simulation. In our model, a supervisory controller for the battery energy storage system (BESS) commands different battery systems, telling them when to charge, discharge, or remain idle. Furthermore, each of four batteries are assigned to individual aggregated load center (ALC), representative of various loads one might encounter on a ship (e.g. radars, lights, motors, etc). Finally, each battery and ALC connect to a common dc bus through a dc-to-dc converter, creating a notional four-zone medium voltage dc architecture. Once trained, the BESS algorithm is deployed into the simulation using specialized hardware (i.e. GTSOC) to evaluate its performance. If the performance is unsatisfactory, the algorithm learns from it and improves itself. If the results meet all expectations, however, the algorithms can now be safely tested and deployed in physical hardware with less risk. The ease of deploying algorithms through SIL allows for the direct comparison between any number of ML algorithms to be done in a fair, cheap, and effective method. 


NAML 2026

Evaluation of Machine Learning Algorithms using Software in the Loop Testing

model and simulation

real time simulation

software in the loop

validation

machine learning

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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NAML 2021

The 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.


Ever since the first introduction of computers and physics-based models, the US Navy has utilized model and simulation (M&S) techniques to improve designs, develop new technologies, and increase the capabilities of the fleet. As M&S techniques have evolved, so have the applications within the naval community. For example, the introduction of real-time simulations and hardware in the loop testing allowed rapid prototype development and the de-risking of shipboard power systems. Presently, innovations in artificial intelligence (AI) and machine learning (ML) algorithms offer another immense opportunity for the US Navy, as AI/ML could greatly impact how warfighters complete their missions. 
Just like any new technology however, AI/ML must be thoroughly validated before they can be fully accepted. In the case of hardware in the loop testing, decades of experiments showed that the testing of hardware in a virtual environment is just as rigorous as traditional testing methods. Unfortunately, AI/ML does not have a standard deployment method. This creates a challenge in comparing different AI/ML solutions to a problem or evaluating the performance of an agent/algorithm in different scenarios. In this work, the authors propose one potential method by demonstrating the evaluation of machine learning algorithms via software in the loop (SIL) testing, where the algorithm connects directly to a real-time simulation. In our model, a supervisory controller for the battery energy storage system (BESS) commands different battery systems, telling them when to charge, discharge, or remain idle. Furthermore, each of four batteries are assigned to individual aggregated load center (ALC), representative of various loads one might encounter on a ship (e.g. radars, lights, motors, etc). Finally, each battery and ALC connect to a common dc bus through a dc-to-dc converter, creating a notional four-zone medium voltage dc architecture. Once trained, the BESS algorithm is deployed into the simulation using specialized hardware (i.e. GTSOC) to evaluate its performance. If the performance is unsatisfactory, the algorithm learns from it and improves itself. If the results meet all expectations, however, the algorithms can now be safely tested and deployed in physical hardware with less risk. The ease of deploying algorithms through SIL allows for the direct comparison between any number of ML algorithms to be done in a fair, cheap, and effective method. 


technical paper

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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



Accurate underwater object recognition remains challenging due to strong backscatter from the target, environment, and water column, as well as the limited use of polarization in conventional imaging systems. Most existing RGB cameras and LiDARs employ single or dual linear polarizations, leaving the rich interaction between light polarization and submerged objects underexploited. In this work, we present an multi-polarization LiDAR imaging and recognition pipeline that combines novel hardware with turbidity-aware computation. On the hardware side, we develop a metasurface-based LiDAR capable of transmitting and receiving four polarization states spanning linear and circular bases, yielding 16 transmit–receive polarization pair images per acquisition. On the algorithmic side, we introduce a turbidity-aware subset selection (TASS) framework that first performs turbidity-aware normalization (TAN), estimating a scene turbidity index from Stokes statistics (e.g., degree-of-polarization decay and return variance) and reweighting each polarization pair to equalize effective SNR and stabilize polarization structure. Lightweight scores are then computed that jointly capture contrast-to-backscatter ratio, polarization preservation (DoLP/angle-of-polarization stability), and inter-pair diversity (low mutual information). A simple greedy policy selects a budgeted subset of k polarization pairs, which are fused by a compact CNN for recognition. We evaluate our system on a submerged dataset of 10 alphanumeric targets imaged under three discrete turbidity levels and varied geometric configurations. Experiments show that (i) multi-polarization fusion significantly outperforms single-polarization baselines, (ii) models trained on optimized polarization subsets can surpass using all 16 pairs, and (iii) TASS with TAN improves accuracy over naive selection and matches or approaches the performance of full-pair fusion, while being more compute- and acquisition-efficient. Overall, our results demonstrate a practical, budget-conscious path toward turbidity-robust underwater object recognition without modifying existing multi-polarization LiDAR hardware.

Metasurface-Based Polarimetric LiDAR and Turbidity-Aware Fusion for Underwater Imaging

The Naval Innovative Science & Engineering (NISE) project, Localization of Aircraft using Several Shipboard Optical Sensors (LASSOS) addresses the critical need for autonomy and perception in degraded environments to maintain our fleet’s competitive advantage. LASSOS aims to estimate the position of incoming aircraft relative to ships using only Pan-Tilt-Zoom (PTZ) cameras, supporting both flight deck tracking to reduce sailor cognitive load and enable Unmanned Aerial Vehicle (UAV) recovery operations. While current commercial off-the-shelf solutions rely on active sensors like Light Detection and Ranging (LiDAR), Navy operations require Emissions Control (EMCON)-compatible passive sensing solutions.
Our technical approach comprises three key tasks: (1) developing machine learning algorithms for PTZ camera calibration, (2) implementing aircraft detection and tracking using object detection models to maintain aircraft within camera field of view, and (3) combining this information to achieve accurate 3D aircraft localization relative to the ship.
The first task, PTZ camera calibration, has two components: an offline calibration algorithm, and an online calibration algorithm. In the offline algorithm, a dataset is captured of several images from each camera containing a set of calibration patterns at different pan, tilt, and zoom values. This is used to estimate and optimize the intrinsic and extrinsic calibration parameters for each camera. The online algorithm is then used to estimate the calibration parameters in real time based on each camera’s current pan, tilt, and zoom value. The second task tracks aircraft using a YOLO-family object detection model. Once an aircraft is identified, the cameras automatically center the aircraft within its view to maintain a high-quality track. The third task takes the estimated calibration parameters from the first task, and the bounding boxes from the second task to provide a 3D position estimate of the aircraft.
This work directly addresses a critical capability gap for NAVAIR, as several UAV recovery systems currently under investigation require precise positional data of incoming aircraft. By providing this data through passive means, LASSOS enables these recovery systems to function in EMCON environments. Beyond recovery operations, the passive sensing technologies developed through LASSOS can be applied to numerous naval perception challenges, advancing autonomous systems across the fleet.

Localization of Aircraft using Several Shipboard Optical Sensors (LASSOS

Visual anomaly detection identifies content that deviates from a known distribution. This remains a major challenge for modern vision models, such as the Vision Transformer (ViT). ViTs excel at classifying seen classes, but lack a principled mechanism to flag truly novel content. 

We propose using hyperdimensional computing (HDC) to solve the problem of visual anomaly detection. HDC captures information into a hypervector (HV), offering a mathematical representation of human memory. For example, we might have a set of hypervectors that represents the visual appearance of a hammer. Each of these HVs would have 10,000 or more dimensions. HVs can be compared to determine how related each of the concepts are to each other. In our hammer example, we can create a mean class to represent the average HV for a hammer. We can compare tools against this to determine their similarity. High similarity indicates the concepts are strongly related, low similarity might indicate that this is another class of tool (i.e., it is out-of-distribution).

Visual anomaly detection has a wide variety of Naval applications. In this work, we consider the application of visual anomaly detection to shipboard robotics. Visual anomaly detection can be used in a wide variety of scenarios including locating rust on bulkheads, leaking mechanical components, frayed wires, or other tasks such as finding debris or locating spills. 

We create HVs from pre-trained networks, such as a ViT. To create a hypervector, the image is processed by the network. We ignore the classification label and instead use the intermediate features to build the HV. This approach is known as Hyperdimensional Feature Fusion (HDFF) (Wilson et al., 2021) and has been validated on problems like finding out-of-class data from CIFAR-10 as well as problems such as transfer learning and gesture recognition (Tran et al., 2025). 

We use HVs to enable visual anomaly detection on our robotics platform. Here, the robot can build a set of HVs representing the normal appearance of an environment. The observed appearance will then be compared to determine if it is visually anomalous. We demonstrate this approach on the problem of cleaning up spills, in which visual anomaly detection is confined only to the appearance of the ground. Although our results are preliminary, we believe that the success in this domain will translate to other areas of shipboard maintenance.

Hyperdimensional Vision Anomaly Detection for Autonomous Shipboard Robotics

Real-time perception in aerial and maritime domains is difficult because detection-plus-tracking pipelines depend on running detectors at every frame, making them slow and computationally expensive. To address this, we develop and present a suite of data-propagation strategies that allow detections to be reused or predicted across intermediate frames, enabling stronger detectors to run less frequently while maintaining accuracy and recall. Data propagation works by using previously computed detections and either copying them forward or updating them through geometric or appearance-based cues, effectively reducing detector load and increasing FPS. We study three cases: (1) Copy Strategy, which directly propagates prior detections; (2) Motion Strategy, which updates detections using a Kalman filter to estimate future positions; and (3) Copy-or-Motion with Re-ID, which selects the better propagation path based on feature similarity from a re-identification model. We evaluate these techniques on challenging aerial and maritime videos featuring diverse object sizes, motion blur, and dynamic backgrounds.
Our results show that no single strategy dominates across all conditions. Copy propagation struggles with small or fast objects but performs well for medium to large targets in slow-moving defense scenarios such as ships or aircraft. Motion propagation is more resilient to rapid movement but can drift with irregular motion patterns. Surprisingly, the combined Re-ID–based approach does not consistently outperform the simpler methods; reliance on feature matching increases latency and reduces the geometric advantages of motion modeling. Through qualitative and quantitative comparisons, we demonstrate how data propagation significantly boosts FPS while preserving detector quality. We also provide practical insights on selecting the appropriate propagation strategy based on object scale, movement characteristics, and application requirements, offering a foundation for building efficient real-time perception pipelines.


Data Propagation Techniques for Real-Time Tracking in Aerial and Maritime Scenarios

Recently, spatial computing and novel view rendering have gained significant traction with organizations increasingly investing R&D efforts in these technologies to develop applications for simulation, visualization and navigation. As part of the Intelligence Advanced Research Projects Activity’s (IARPA) WRIVA program, Accenture has been developing advanced novel view synthesis methodologies to tackle 3D rendering and site modeling at speed and at scale using incidental imagery to support mission planning and preparation, remote asset monitoring and inspection, visualization and virtual reality.

One of the challenges of current methodologies is their scalability and computational needs. Structure from motion pipelines contain spatial matching that calculates the proximity of all cameras using GPS information to generate a list of possible spatial nearest neighbors. It then proceeds to use salient image features and Nearest Neighbor Ratio Test Filtering to find inlier matches. This process scales quadratically with the number of input images, making the ingestion of thousands of input images computationally infeasible. To address the complex task of creating large reconstructions in a timely manner, we use an improved pipeline that addresses the bottlenecks present in feature extraction, feature matching, and reconstruction. 

For a scene with thousands of images and GPS data, a more robust approach would use hierarchical localization with a better feature extractor to account for large perspective changes and appearance changes. In addition, a better matcher would use better descriptors and context aware attention mechanisms. However, even with better features and matches, exhaustive image matching is not feasible for practical applications. Therefore, we first create a list of coarse matches using GPS data and then match robustly. With noisy GPS data, we assume up to a maximum of 20 meters error, with an average of 3-6 meters. However, the possible spatial neighbors per image remains vast. As a result, we use visual place recognition (VPR) to create a list of coarse matches. VPR models can run very quickly and account for time variations within images. We limit the number of matches to 25 per image by generating similarity scores between all the global descriptors of input images. Afterward, we perform geometric verification. Lastly, we use reconstruction methods that estimate the camera pose and structure for all images simultaneously and perform bundle adjustment in scalable steps.

In this talk we describe how we are researching state-of-the-art capabilities in 3D reconstruction and site modeling (e.g., Gaussian Splatting) to address scale. We will discuss how we are using available image metadata, camera pose estimation and diffusion models to build 3D site representations and reconstruct scenes given large number of input images from unconstrained sensor conditions.

BELLA - A Framework for Large Scale Novel View Rendering

Our adversaries are using low-cost unmanned drones, leveraging a highly adaptable and capable platform for a variety of tasks from intelligence gathering to precision strikes. These drones are transforming the traditional battlefield, offering military advantage at the fraction of the cost of traditional systems. From a counter-drone perspective, they pose a significant challenge because they are a constant changing mix of commercial platforms with modified payloads. Statically built AI models for counter-drone cannot keep pace.

Future conflicts for counter-drone will require the US Navy to address the agility of its AI-system with continuous model adaptation at the tactical edge. The AI and MLOps solution must be operational where communication is denied or degraded and where static, pre-trained models quickly fall behind evolving threats. Recent Navy efforts in Project AMMO/Overmatch have demonstrated the reduction of model fielding time from months to days, with the adoption of a robust MLOps workflow. Within this project, we have demonstrated model compression and speedup while maintaining model accuracy and while staying within SWaP constraints. Furthermore, we demonstrated real-time field updatable models with watermarks and encryption to secure against adversary exploitations.

Beyond Project AMMO and under Army Project Linchpin, we have further reduced the model update time to minutes, and with MLOps software embedded in a field portable tactical kit. Our Linchpin work points to a concept of operations where threat libraries and models evolve in near real time, and operational in networked denied environments. New data on enemy targets can be captured and labeled with AI-assistance, and new targeting algorithms can be tuned quickly. We leverage embeddings from foundation models to reduce overall model training time. This workflow is built for operators, and can serve as an adaptive counter-drone pipeline. We plan to provide a demonstration of this technology at NAML, as an approach to refresh counter-UAS, swarm, ISR, and electronic warfare models as fast as the threat evolves. Such a capability can lock in a sustained battlefield advantage that matches the SecWar emphasis on speed, adaptability, and AI-enabled decision dominance.


Continuous model adaptation at the tactical edge

High-Performance Computing (HPC) systems generate massive numerical simulation data that domain experts struggle to analyze efficiently, while Large Language Models (LLMs) excel at natural language but cannot interpret raw numerical outputs. We present a novel, domain-agnostic multimodal pipeline that bridges this gap by automatically transforming numerical simulation data into natural language, without requiring manual annotation, visualization, or expert labeling.

Our proposed pipeline includes a multi-modal model, obtained by fusing a state-of-the-art LLM (such as one from the Mistral or Llama families) with an encoding pipeline, which projects a numerical sample onto a meaningful latent space. The two components are fused by means of a cross-attention layer, which connects the textual and numerical information.

An integral part of the research is the training methodology, which includes the generation of representative data samples by means of numerical analysis tools. The data samples aim at linking the latent representation of the numerical data to quantities of interest for scientists, and their natural language descriptions.

As a proof of concept, we present an ongoing project, which is part of Destination Earth, European Commission’s flagship initiative aimed at creating an accurate digital twin of the Earth system. The project is a weather specialization of our suggested approach, fusing an existing AI model (AIFS, developed by ECMWF) with an open source LLM (from the Mistral family), to enable real-time analysis of numerical weather prediction data.

As this project is still in progress, we will highlight its achievements and the challenges we are currently facing.


Designing a multimodal LLM to analyze numerical data

Many US Government agencies have complex system diagnostic workflows that need automation. However, their systems are typically unable to support Large Language Models (LLMs) due to a lack of GPU hardware infrastructure. Adding GPUs to such environments is difficult and cloud providers are not viable due to DDIL requirements. Instead, they rely on IT experts to interpret system logs and remediate issues. This places strain on IT experts in mission-critical environments and can delay error resolution time. Existing automation methods rely on simple machine learning and brittle parsing pipelines that lack extensibility and adaptability to new log patterns without extensive re-engineering. These are limitations that LLMs overcome through few-shot learning and flexible reasoning.
We propose using a modular Knowledge Distillation (KD) framework along with diagnostic workflow decomposition to balance each agency’s adaptable automation needs with their hardware constraints. KD is a process in which a larger LLM is used as a teacher to train smaller and cheaper student models that will replicate the teacher’s capabilities. By working with Subject Matter Experts (SMEs) to decompose troubleshooting workflows into specific subtasks, we allow the teacher to adapt to the subtasks through prompting and few-shot learning. Next, the teacher generates labeled synthetic data to fine-tune each student model for a specific subtask. For tougher subtasks, additional KD techniques (logit KD, counterfactual distillation, etc.) are leveraged depending on the unique complexities of the subtask. The resulting student models can then be deployed in environments lacking GPU support and may be reused modularly across other overlapping troubleshooting workflows.
To verify the success of our approach, we work with SMEs to develop sets of sample input log data with associated acceptable resolutions for each workflow. We use evaluation techniques such as semantic similarity, LLM-as-a-judge, and domain-specific heuristics to assign quantitative evaluation values to the results. We first show that the teacher model achieves acceptable evaluation scores across an SME-identified troubleshooting workflow as a baseline. We then show that the student models achieve comparable evaluation scores when remediating that same error. For each subtask, we also use the teacher model to develop gold standard datasets of labeled test data to assess accuracy and precision of each student model individually.
Our work provides a path toward modular diagnostic frameworks that can be deployed securely in US Government environments that lack resources. Our framework enables SMEs to be more efficient by reducing their cognitive load when solving these complex problems and enables domain workers who are not yet “experts” (e.g. IT3 right out of IT school) to be more effective when approaching these problems. In both scenarios, labor cost and system downtime for problem resolution are therefore minimized.

Modular Knowledge Distillation for System Diagnostics on Edge Devices

The U.S. Marine Corps (USMC) faces significant challenges in adapting its operational frameworks to embrace modern technologies, notably in the integration of advanced data sciences and AI/ML. For example, maintenance cycle challenges include: 1) excessive administration overhead, Marines spend more time on data entry and status updates then hands on maintenance; 2) fragmented data sources, critical information is often scattered across multiple systems; 3) tribal knowledge, critical know how often resides with a few experienced maintainers rather than being documented; 4) manual re-entry errors, information that is copied between systems without automation often gets mistyped.
To leverage advanced data sciences and AI/ML, we research and implement a prototype tool called AI Data integration & Automation for Maintenance (ADAM). The expectation of ADAM is to 1) act as the data set SME, providing clear insights and ensuring all conclusions are based on established standards; 2) develop, analyze, and summarize Excel reports to provide clear insights that inform leadership decisions, resource allocation, and mission readiness; 3) perform trend analysis to identify issues and patterns, using insights to improve preventive maintenance, anticipate risks, and enhance equipment reliability.
In this paper, we will review and analyze methods to address these challenges and expectations as follows:
• Apply and compare natural language analysis methods including large language models (LLMs, Llama3:8B, Qwen3:30B, GPT-5), retrieval augmented generation (RAG), and lexical link analysis (LLA), extract patterns from unstructured data to characterize the risks. 
• Apply collaborative learning agents (CLAs) to analyze distributed and dynamic data without moving raw data to a central location by using a network of autonomous learning agents that collaboratively discover patterns, anomalies, and emerging concepts. 
• Utilize RAG severs and explore context and machine learning for the Global Combat Support System Marine Corps (GCSS-MC) to provide context-aware decision support and predictive analytics for logistics and maintenance.
• Design testing procedures for varying complexity designed to assess each model’s consistency, accuracy, reasoning, and prompt-following, while evaluating how performance changes with prompt quality with and without context.

We will demonstrate the system using the use case from the Electronics Maintenance Platoon, 11th Marines Regiment HQ Battery.

Acknowledgement: Thanks to Quantum Intelligence, Inc. and the SBIR contract N6833525G0023 “Developing Collaborative Learning Agents (CLA) System for the USMC Warfighting Systems, & Human Factors Integration.”

Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as others or representing the official policies, either expressed or implied of the U.S. Government.


AI Data integration & Automation For Maintenance

Overview:
Capturing, analyzing, and exploiting lessons from operations allows improvement in NATO technology, tactics, and procedures. In order to utilize AI in the analysis of Lessons Learned, we must be able to recognize positive and negative results. To move towards these capabilities, experiments were performed on tracing the impact of lessons in subsequent doctrine. This paper discusses those experiments within the context of the RTG goals. Working with various versions of doctrine as well as the observations of operational staff during exercises, these experiments attempted to recognize when lessons are reflected in newer versions of doctrine. The development of Large Language Models (LLM) has shown that one of key factors in utilizing AI models is the ability to evaluate the results from a model not only to judge the results, but to improve the models. These experiments provide one piece of the evaluation required to apply AI to the LL process. 
Implementation:
Separating useful feedback from a large repository of Lessons Learned is challenging and time-consuming for NATO analysts. This project experimented with the ways LLMs can bring clarity and focus to their work. 
After cleaning and parsing the data, comparisons of cosine semantic similarity were performed in order to demonstrate the value of using visualizations to present this data. Through heat maps, bipartite graphs, and U-MAP, we were able to show how to simplify the analysis process using these automated processes. 
Additionally, we created knowledge graphs to explore the Lessons Learned repository. Using MemgraphLab and Python scripts, we are able to extract entities and properties from the Lessons Learned and use them to create a knowledge graph that is easy to query and use to quickly see patterns.
We envision future work to consist of creating a People Knowledge Graph that will use Natural Language Processing (NLP) techniques to extract skills from a Subject Matter Expert database and to connect these to Lessons Learned. This will facilitate interdisciplinary discussion and collaboration. 
Summary: 
The current presentation consists of our early exploratory work with this rich and multi-layered Lessons Learned database. We present our initial ideas, results, as well as our future plans. 


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