United States

Practitioners of AI/ML who are applying their skills to RF problems often intially focus on the signal detection and classification problem. Though this is a rich and interesting problem, this talk seeks to provide an overview of several alternative applications of ML techniques that go beyond signal detection and classification. These include learned signal processing, generative AI for channel modeling, and fully-learned communications waveforms.

NAML 2024

RFML Beyond Signal Classification: ML Signal Processing, Generative AI for Channel Modeling, and Learned Comms for 6G

generative ai

signal processing

technical paper

### Welcome to the 8th 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.

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

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific 

**[More about NAML 2024](https://sites.google.com/go.spawar.navy.mil/naml/)**

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The 8th 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.


Making Large Language Models (LLMs) Accessible, The need for an accessible and user-friendly platform for working with LLMs. 



CPU is all you need

Typically, effective object detection in images requires thousands or tens of thousands of labeled instances. Crowd-sourced data has been used to train object detectors for commercial purposes, but the DoD cannot use this same approach to train neural networks with supervised learning. Either there is an insufficient number of instances of DOD-relevant data, or curating and labeling that many images is too cost and time prohibitive to be of use. ​

For this reason, few-shot object detection can be used to limit training data to tens or hundreds of instances rather than thousands upon thousands. Specifically, our experiments adapt few-shot object detection to the Naval domain. We set up two experiments where in the first experiment, we use a large, common dataset to train a base network followed by training with a small sub-group of novel classes of data; and, in the second experiment, we test another few-shot network, DE-Vit, which can use few-shot methods to find new objects in an image. ​

There are many methods that can be used to train neural networks with fewer data. These experiments are conducted as a part of the SAVANT project—which uses an ensemble of semi-supervised methods to build upon datasets. It will serve as a test bed for semi-supervised methods while creating some much-needed labeled Naval data.

Adapting Few-Shot Learning To The Naval Domain

Naval aviation assets on aircraft carriers such as aircraft, catapults and arresting gear are tracked through information systems that provide situational awareness to sailors. The current concept of operations requires highly skilled workstation operators to manage everything from aircraft status to flight schedules to keep information up to date. Operators receive information over radio communication channels from flight deck personnel. This process requires the operator to be dedicated to listening to updates and manually entering what they hear into workstations. This project developed an automated speech recognition prototype that can automatically listen into the audio communication pipeline and add information into computer systems.  
A speech recognition pipeline was created to recognize the standard phrases spoken by aircraft carrier flight deck personnel. Outputs can be used to correctly identify the actions that need to be performed to update computer systems automatically. The pipeline includes speech to text Automatic Speech Recognition (ASR) models and speech enhancement followed by post processing and similarity matching techniques to increase accuracy. Recursion was implemented to re-run the pipeline with a larger speech model if the result does not match a standard flight deck phrase. The team made use of Whisper, a transformer-based model with various model sizes ranging from 39 million to 1.56 billion trained parameters. The pipeline was tested in a reverberation chamber using flight deck phrases under various noise levels and model sizes. 
The team is currently working on fine-tuning the model and creating synthetic data. Data generation used by the team include the ability to fabricate semi-realistic speech in 108 varying voices and dialects. The team has tested fine-tuning efforts on a hold-out percentage of the dataset but has plans to continue fine-tuning the whisper model on both real-world and synthetic data to note results on increased model performance. While high-decibel noisy recordings were obtained from a laboratory, the researchers hope to obtain more real-world data and ultimately test the system either in a simulated environment or the flight deck environment. 


Aircraft Carrier Flight Deck Information Updates using Speech Recognition 

we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviates issues related to a priori identification of mechanis- tic system models that may become deficient under evolving system behaviors and/or shifting, unanticipated, environmental influences. Our proposed learning-based platform comprises a deep Koopman system model and a change point detector that provides guidance on domain shifts prompting relearning under severe exogenous/endogenous perturbations. Motion control of the autonomous system is achieved via an optimal controller design. The Koopman linearized model naturally lends itself to a linear–quadratic regulator (LQR) control design. We propose the C3DKL control architecture “Cascade Control with Change Point Detection and Deep Koopman Learning”. The framework is verified in both simulation and real experiments.

C3DKL: Cascade Control with Change Point Detection and Deep Koopman Learning for Time-varying Systems

Federated learning (FL) is a distributed machine learning paradigm that enables clients to collaborate without accessing, infringing upon, or leaking user raw data by sharing only model parameters. Multimodal automatic target recognition (ATR) in FL aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities (e.g., sensors measuring pressure, motion, and other types of data). However, key challenges to multimodal FL remain unaddressed, particularly in heterogeneous network settings: (i) the set of modalities collected by each client will be diverse, and (ii) communication limitations prevent clients from uploading all their locally trained modality models to the server. In this study, we propose multimodal fusion Federated learning with joint Modality and Client selection (mmFedMC), a new FL framework for multimodal ATR that can tackle the above-mentioned challenges. The joint selection algorithm incorporates two main components: (a) A modality selection criterion for each client, which weighs (i) the impact of the modality, gauged by Shapley value analysis, (ii) the modality model size as a gauge of communication overhead, against (iii) the frequency of modality model updates, denoted recency, to enhance generalizability. This allows mmFedMC to flexibly balance performance against communication costs, depending on resource constraints and application requirements. (b) A client selection for the server is performed on the basis of the cross-entropy of prediction as a metric. This involves selecting a subset of clients for uploading and aggregating models, which further reduces communication overhead and optimizes the overall process. Experiments on a real-world dataset with six modalities demonstrate that mmFedMC achieves 98.19\% accuracy with a communication overhead of only 5MB per client. This represents an approximate 10\% increase in accuracy compared to random modality or client selection. Moreover, in comparison to other state-of-the-art methods that do not utilize a selection mechanism, mmFedMC enhances accuracy by a factor of 2x to 4x. Alternatively, to achieve comparable levels of accuracy, other methods require approximately 20x more communication overhead, i.e., about 100MB per client. Beyond the trade-off between performance and communication overhead, mmFedMC also offers an interpretable representation of the modality impact during the FL process. In the initial stages of FL, most modalities exhibit similar impacts. However, as the communication rounds progress, modalities with larger feature sets and complex models play a subordinate role in modality selection due to their higher communication costs. With the advancement of FL, more straightforward modalities that continue to convey substantial information emerge as primary contributors.

Communication-Efficient Federated Learning for Multimodal Automatic Target Recognition

The signal landscape is constantly changing as technology evolves. However, much of the work done in Few-shot Learning (FSL) has been done in the image domain. The goal of this study is to evaluate the effectiveness of various FSL methods from the image domain in the signal domain. We were able to transfer FSL methods into the image domain using an open-source signals dataset. Using one-shot learning techniques, Deep Learning models are able to identify signals of unseen modulation types with performance close to baseline classical classification tasks.

Novel Signal Classification using Machine Learning

The study will analyze how to apply USMC data governance advances to JADC2. The long term objective is to advance Project OVERMATCH and JADC2 with respect to improving processes for leveraging data and machine learning to achieve decision advantage.
The immediate study objective is to answer the following questions: 1. How can recent USMC advances in data governance be applied to advance Project OVERMATCH and JADC2? 2. How can formalized data governance processes and automation improve the quality of decisions and/or speed up data-driven C2? 3. What new data sources hold the most value for improving C2 and decision advantage? Navy and Marine Corps officers will be part of the research team. Leveraging faculty expertise and consulting with subject matter experts, students will carry out literature surveys and case studies to determine answers to the study questions and assess the merits of new approaches addressed by the study. This study is part of the Navy/USMC contribution to JADC2 and has potential to impact capability development & integration efforts to related programs of record.

Data Governance in Support of Joint C2

Jupiter - AI Enablement

Deep neural network sensitivity to varied radar waveform properties for radar classification and domain adaptation

You don't need to understand how to fly a plane or design a plane to understand what aviation can do for war. The same thing with artificial intelligence.
This talk walks you through strategic alignment, how the Marine Corps is thinking about implementing AI across the Corps and in support of both Marine Corps operations in the broader naval mission. 

Marine Corps is very good at fighting. But if you look at how we fought for the last 50 years compared to where we could be in the future, we certainly can fight smart. The ability of MC to pull in data, to pull from different sources, from different venues and things, apply analytics, and help us to decision advantage across a range of what we do. Whether that is at the pointy end of the stick, whether that's recruiting, whether that's our internal talent management, whether that's logistics, whether that's supply system,the ability for us to leverage some of these tools is not near infinite, but we have an enormous amount to do and we want to overall be able to fight smart. But right now we're not and this is our path to how we're gonna get to that to take into account this the emerging technology where we think this not only where AI is today, but where we think AI is going over the next few years and then in the beyond into the 2030.

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RFML Beyond Signal Classification: ML Signal Processing, Generative AI for Channel Modeling, and Learned Comms for 6G

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