United States

The space domain is becoming ever more congested and complex. The number of satellite launches is growing exponentially; commercialisation of various space-based imaging services and the number of space-capable nations continues to rise; and adversaries increasingly operate in grey-zones, attempting to evade attribution of their activities. Simultaneously, the variety and quantity of available data is growing, providing a complex challenge for space analysists trying to build an accurate picture. Machine Learning (ML) techniques can offer assistance to analysts in managing these burdens: ML can exploit excess data to provide timesaving, wider-reaching, or novel capability, and release human effort to focus on complex or higher-priority issues.

This presentation demonstrates the feasibility and effectiveness of machine learning models for satellite characterisation. Dstl has developed models to predict the operational status and the bus (structural frame type) of satellites using ground-based time-series observations of reflected sun-light or reflected radar. We found classical ML models (e.g. Random Forests) performed as well as Deep Learning approaches: our LSTM (long short-term memory) models struggled to retain their high performance when generalising to non-iid (independent identically distributed), out-of-distribution samples. The full range of characterisation tasks is significantly broader than those investigated here and considerable scope remains for further low-TRL research.

Data was a critical and limiting factor throughout this work. Based on the sources available, Wide Field Lens Array telescopes offered the best data for LEO characterisation while traditional commercial world-wide networks of Newtonian telescopes offered the best data for GEO characterisation. Open-source labels were not always available to high enough accuracy, limiting model performance in some cases. Furthermore, open-source labels are not openly archived, complicating the use of historic observations.

NAML 2022

Machine Learning for Satellite Characterisation

space

satellite

machine learning

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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. 
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NAML 2026

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. 



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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

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.

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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


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Registration for this workshop has now closed.

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. 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.

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

To build and strengthen collaborations within the defense research community



The work presented describes how real-time geospatial image processing can be accomplished on a SWaP (size, weight, and power)-constrained edge system. Traditionally, the data processing requirements for these workflows end-to-end are compute-intensive, and therefore challenging to deploy to power-constrained edge-based hardware. The edge platform we use is a self-hosted BF2 DPU paired with an A100 GPU, allowing for a low-power, high-performance edge solution without a host CPU. We'll demonstrate how common geospatial processing workloads such as orthorectification can be GPU-accelerated and launched from our edge BF2 system.

High Performance Geospatial Image Processing at the Edge

It is critical to always have an accurate estimate of the position of aircraft on the carrier deck. Currently, determining the pose of aircraft is done via visual inspection by several sailors who then update a digital interface with any changes to the position or status of aircraft. This task is labor intensive, requires focus for extended periods of time, and requires multiple personnel stationed during all flight operations. PATRIOT (Panoramic Asset Tracking of Real-time Information for the Ouija Tabletop) is a research endeavor to apply computer vision techniques to existing camera feeds from the carrier to identify and track aircraft, personnel, and vehicles. This work focuses on two branches used in the overall PATRIOT pipeline that estimate the pose of aircraft from synthetic and real videos: keypoint-based techniques and bounding box-based techniques. Several state-of-the-art keypoint-based methods (High-Resolution Net, Higher High-Resolution Net, and OpenPifPaf) and accompanying pose estimation algorithms are compared with a bounding box-based method using Faster R-CNN and an encoder-decoder model.

Pose Estimation Techniques for PATRIOT (Panoramic Asset Tracking of Real-time Information for the Ouija Tabletop)

The intensity of atmospheric turbulence has a profound effect on the performance of laser weapon systems. Proper configuration and operation of these systems requires nearly real-time knowledge of the atmosphere’s turbulent intensity. Determination of this characteristics is, however, difficult in operational settings. Models exist based on macro-meteorological measurements but these have been shown to have questionable accuracy, especially in the maritime and near-maritime environment. Current techniques for directly determining atmospheric turbulent intensity are also impractical in an operational setting, typically requiring two-sided systems with both a transmitter and receiver, one at the source and one at the target. For these reasons alternative techniques for determining the intensity of atmospheric intensity are desirable. Specifically, a one-sided system, only requiring information acquisition from the source side would be particularly attractive.
The disruption of laser weapon performance due to atmospheric turbulence affects other phenomenon based on the propagation of light, namely imaging. As turbulent intensity increases, the quality of images taken through the atmosphere (a one-sided technique) decreases. Consequently, images blurred by atmospheric turbulence carry information about its intensity. As atmospheric turbulence increases, image quality decreases. The current work uses convolutional neural networks (CNN), in combination with a synthetically generated data set of turbulently blurred images, to predict turbulent intensity from only blurred images. Specifically, a training and validation data set is created using a pristine image which is subsequently blurred using software that simulates the effect of the turbulent atmosphere. The data is then used to train CNNs to predict the turbulent intensity of “unknown” images in the validation set. The results are encouraging and demonstrate the ability of CNNs to predict the intensity of atmospheric turbulence using only a blurred image.

Determining the Intensity of Atmospheric Turbulence Using Blurred Images and a CNN

Many — if not most — applications of artificial intelligence (AI) involve accurately tracking and identifying objects in the physical world. For example, shipboard systems could autonomously recognize and track nearby vessels, self-driving vehicles could identify and track cars and pedestrians, and search-and-rescue systems could automatically locate and track the lost or wounded. However, current tracking techniques have significant weaknesses that limit their applicability and robustness. Most machine learning systems require large-scale training datasets that are difficult or impossible to collect in many applications. These systems are also easily confused by novel scenarios and are vulnerable to adversarial attacks. To overcome these limitations, we draw inspiration from biological visual systems, which are remarkably robust in their ability to track and identify objects, even those that have not been seen before. In this talk, we will describe our current progress building biologically-inspired tracking systems. Our ultimate goal is to lay the groundwork to develop potentially revolutionary visual systems that are more robust and generalizable than today’s techniques.

This work was sponsored by a grant from Naval Surface Warfare Center Crane Division.

Building biologically-inspired object tracking systems

In the current rapidly developing environment of both technologies and techniques in electronic warfare (EW), it is essential for modeling and simulation (M&S) frameworks to keep up with the pace. When it comes to creating an accurate M&S environment, data is king. Collecting the necessary data through flight tests is not feasible due to the cost, availability of assets, and need for rapid turnaround. 
There are many tools that allow for quick creation of RF test data for the M&S environment, but the difference between testing on lab generated data and the real-world data can be the difference between life and death in the field. This is especially true for future cognitive EW systems which, as with all machine learning (ML) systems, will perform worse on noisy real-world data. The ML technique called Style Transfer is a powerful tool that can be leveraged in the EW M&S domain to improve the testing and evaluation (T&E) of the different systems involved in the kill chain.
Style transfer was developed as a tool for capturing the art styles of different prominent artists and transferring that style to other paintings and photographs. This model can show you what the Mona Lisa would look like if painted by Picasso, van Gogh, or Monet. This same technique can be used to modify lab generated, digitized radio frequency (RF) data to look more realistic. The styles that can be captured in this domain include the emitters, environmental noise, and receivers. Multiple styles can be applied to a single signal, which would allow a “clean” lab signal to be modified to look as if it were generated from a specific emitter, traveled through a noisy environment, and was received by a specific receiver. This technique can be applied to any digitized RF data, so it can be used to improve existing data sets. Also, with the rapid improvement of hardware, a model using this technique may be able to act as a real-time filter on generated data in M&S frameworks.
Style transfer provides a way to improve the realism of lab-generated RF data to improve the confidence in the results of T&E events. The technique is useful not only for current systems, but will continue to be relevant as new systems enter the battlefield, including cognitive systems.

Generating Realistic RF Test Data Through Style Transfer

Deep learning algorithms are powerful and present an opportunity to achieve high performance in computer vision applications. Unfortunately, these algorithms also require large quantities of data that can be difficult to assemble, particularly in the case of Defense applications. Obtaining conventional imagery that is relevant to defense applications is expensive and time-consuming. Synthetic data, therefore, has emerged not only as a viable option, but as a necessary component of defense algorithm development strategy. The U.S. Army Futures Command DEVCOM C5ISR Center, Research and Technology Integration Directorate, Modeling and Simulation Division is developing a variety of synthetic data generation capabilities. One of these approaches utilizes fully synthetic imagery generated by physics engines with high-fidelity inputs and models. In particular, we have created an IR image generation pipeline using the UnrealEngine (UE4) and an IR plugin. In order to evaluate the suitability of this synthetic imagery for algorithm training, we used it to train a version of YOLO to detect and classify real targets in field-collected imagery. This presentation outlines the image generation pipeline, the performance of various iterations of the UE4 imagery when evaluated against real data, and future steps for synthetic imagery development.

Evaluation of IR Imagery Generated by UnrealEngine for Algorithm Training

How best to leverage small datasets for machine learning tasks is an active area of research. Synthetic data has the potential to alleviate object rarity and long-tail distributions, provided the synthetic data introduces more signal than noise into the system. The IQT Labs Synthesizing Robustness project aimed to explore whether domain adaptation strategies (attempting to make the synthetic data look more “realistic”) can improve the efficacy of the synthetic data when it comes to localizing rare objects. In this project we explored generative adversarial network (GAN) techniques for domain adaptation on the publicly available RarePlanes satellite imagery dataset. 

Over a number of experiments we quantified the detection performance for rare aircraft as a function of training size per object class. We also note that strategically augmenting the real dataset is nearly as effective as adding synthetic data in the quest to improve the detection or rare object classes. Intriguingly, in certain cases, confusion between similar-appearing object classes is a more important factor than training dataset size in determining detection performance. Finally, we share some of the lessons learned from this project; for example: fully extracting the utility of synthetic data is a tricky and nuanced process, requiring effort and creativity.

Synthesizing Robustness: On The Nuances of Extracting Utility from Synthetic Data

Scalable synthetic sensor simulation is an important yet challenging open problem for ROV simulators to bridge the "reality gap" and "domain randomization gap" for machine learning and training. Utilizing machine vision techniques Blue Ring Imaging and SeeByte are constructing a pipeline for creation of synthetic underwater assets from camera and sonar data which is photoreal, randomized, and reflect the diversity of underwater environments for ROV CONOPs. In this talk Casey Sapp, CEO of Blue Ring Imaging, and Vincente Nguyen of SeeByte will provide a high level overview of synthetic data generation of sonar data using GANs (Generative Adversarial Networks) for ROV simulation and reinforcement learning.

Synthetic Data Generation for UUV Reinforcement Learning

The Defence Science and Technology Group (DST Group) have been assisting Navy to develop a new Intelligence, Surveillance and Reconnaissance (ISR) and targeting capability on-board ships. The research began as an attempt to automatically classify ships with Convolutional Neural Network (CNN) technology from images provided by Uninhabited Aerial Vehicle (UAV) camera streams. This effort expanded into an investigation surrounding how to deploy Machine Learning (ML) system(s) that would eventually be integrated into existing Defence combat and mission systems. After establishing an Artificial Intelligence (AI) eco-system the focus shifted towards using ML that would reduce the communications bandwidth required to share visual information across tactical networks. This naturally morphed into establishing robust and reliable models that operate within a defence context. These models would need to be resilient to adversarial attacks and present information to operators in language that was intuitive to a human and machine team (using eXplainable AI). DST Group has collaborated with the University of Adelaide and Defence to create an ensemble of models to dynamically recognise ships at sea in near real-time. At present this application includes: ABCNet; Deepsort; Detectron2; Detr; ProtoPNet; StDan; Recurrent Neural Networks (RNNs) to construct an attention network; a sophisticated Transformer network; and additional AI techniques to support interoperability. At present this research is an evolving concept demonstrator, aimed at generating visual tracks in near real-time together with an intuitive text description about each maritime asset detected within the scene. This talk will discuss the journey and demonstrate the progress made.

AI-Enabled Tactical Information Exploitation

Task scheduling on multiple channels is a mature field of research and is a critical component of modern multifunction radars. Management of the radar timeline can significantly impact the overall performance by directly affecting the ability to detect and track targets. Determining the optimal schedule given the channel availabilities and a set of search/track tasks can require a prohibitive amount of computation, especially when the radar scheduler needs to operate in real time. Suboptimal solutions can be generated with fast algorithms, including traditional methods such as Monte Carlo tree search; however, if the number of tasks is large, the search space may be too voluminous and the required performance may not be met. To enable fast, high-performance schedulers, machine learning can be employed. If there is statistical similarity among the different problems the scheduler solves, then the scheduler can learn from previous problems and operate on novel ones using knowledge not exploited by a classic scheduler. Given that optimal schedulers such as Branch-and-Bound are available, a sensible approach is to use supervised learning - saved scheduling problems can be solved offline and used to generate labeled data. This work will demonstrate how supervised learning can be used to create policy neural networks that outperform various traditional schedulers (even without integration into search algorithms) while still maintaining fast execution for real-time implementation. The scheduling problem will be cast into the form of a Markov decision process. A specific focus will be how the tasks are encoded into the observation space; it will be shown that intelligent design of this "feature extraction" can allow even policies with modest degrees-of-freedom to perform well. Different reward functions will also be considered. Implementation of the framework and algorithms discussed are available as a Python package, "task-scheduling" (https://spork.nre.navy.mil/nrl-radar/CRM/task-scheduling). Access to the source code is restricted to U.S. DoD members with accounts on NRE Fusion.

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