United States

NAML 2023

Estimating Sound Speed Profiles Using Vessel Source Signals of Opportunity

technical paper

### Welcome to the 7th annual workshop on Naval Applications of Machine Learning!

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<br><br>

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To access the site please register [**here**](https://events.r20.constantcontact.com/register/eventReg?oeidk=a07ejmzkc5g71584a82&oseq=&c=&ch=). 

The 7th 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.


RippleGo is an AI-based voyage planner for the Inland Water Transportation industry.  In this talk, two AI-based technologies will be discussed and how each of the technologies improve safety and efficiency along the waterways.  One is a river level prediction model that forecasts river levels and the other is a travel time prediction model that forecasts time taken to go from a starting point to destination along the inland waterways.  Combining the 2 predictive technologies and other marine data sets, TRABUS developed a software-as-a-service application that enables operators in the inland waterways to plan and execute their voyages, safely and efficiently.  The talk will provide an overview of some of the core AI-driven capabilities of RippleGo.

RippleGo - First AI-Based Voyage Planning and Predictive Analytics Software for the US Inland Waterways

Coupled dynamical systems, especially those comprised of hysteretic elements, possess remarkable behavioral similarities to some aspects of neural activity in living systems. While some features of this similarity have been explored, the results have not yet been leveraged to improve machine learning techniques or artificial neural networks. In this work, a dynamical system of coupled overdamped bistable elements subject to a noise floor assists in information transfer. With the noise allowing the system elements to switch randomly between their stable states, the system response is quantified via a steady state probability distribution. A simple five element realization of this network is used to produce the analog equivalent of an XOR gate, an “XOR transform”, through the proper choice of coupling coefficients and controllable external biases. The demonstration of the XOR transform indicates the ability of the system to synthesize more general functions, a necessary prerequisite for machine learning and computing applications. Finally, a silicon implementation is proposed and simulated using a verified process model that could be fabricated to generate a working analog computing system.

Noise Driven Coupled Dynamic Systems–Towards Low Power Pattern Recognition and Machine Learning

Ensuring the success of future multi-domain operations (MDO) against a heavily militarized and techno-logically advanced adversary is a critical need for the Department of Defense. Raytheon Technologies (RTX) seeks to increase the productivity and impact of the warfighter in such peer-to-peer conflicts by reducing operator workload through sensor autonomy to speed the observe-orient-decide-act (OODA) loop and shorten the target kill chain. In this construct, the warfighter or analyst operates over-the-loop, focusing on complex mission decisions, and maintains decision-making control while supported by trusted AI/ML-enabled cognitive aids performing data intensive tasks within-the-loop that automatically process sensory data to produce decision-relevant information at the edge. This presentation introduces an autonomy vision called Cognitive Aids to Sensor Processing, Exploitation and Response, or CASPERTM and provides an overview of the Raytheon Applications of AI to Defense Systems or RAAIDS IR&D. For this effort, RTX has partnered with Virginia Polytechnic Institute (VT), Southern Methodist University (SMU) and the University of Texas San Antonio (UTSA). VT and SMU are working collaboratively to develop RL/ML-enabled algorithms that can autonomously control edge sensor operations to achieve greater automated detection, classification, and tracking of objects, optimizing both within an individual sensing system and across multiple systems and platforms that are sensing cooperatively. UTSA brings expertise in human-machine teaming and user experience design, which is not just a software development problem, but involves key disciplines such as cognitive science and psychology, human factors engineering, computer science, communication design, interaction design and usability engineering.

Raytheon Application of AI to Defense Systems

Deep Learning Physics Tools: Being Alert to Fatigue

"Jupiter, the Naval Enterprise Data and Analytics Environment, enables Sailors, Marines, and Civilians to integrate, share, and exploit data at speed and scale across the Warfighting, Business, and Readiness pillars for operational advantage and increased efficiency.  The Department of Navy Chief Data Officer and the Jupiter platform enable Artifical Intelligence across the Department of Navy.
"

Jupiter - AI and Data Enablement

Multi-Platform Space-Based AIS Fused RF Machine Learning

"Most AI/ML practitioners would agree that one of the top challenges they face is a lack of real-world data to use for research, training, and development. Scientists and engineers from NIWC Atlantic have been working to address this problem by supporting Task Force 59 to develop an infrastructure for collecting raw sensor data during maritime exercises and moving that data into a secure cloud store for later use by the fleet, S&T, and a variety of other stakeholders.
This presentation will provide an overview of the NIWC Atlantic involvement with Task Force 59 and Digital Horizon 2022, with specific focus on data collection and data lake creation. Engineering for this event was performed on a highly compressed timeline with numerous challenges and unknowns, and we will share the lessons learned which reflect an approach of pragmatism and initiative. 
"

Digital Horizon 2022 - Creating a Data Lake in the Persian Gulf

Small autonomous underwater vehicles operating in complex shallow-water environments must have the ability to find passageways, determine their location, and accomplish their mission goals without external control. Sonar offers the right range of sensing distances for such systems even in the most turbid waters. However, operation in shallow waters and among complex natural and man-made obstacles is very different from the open-water sensing scenarios for which typical sonars have been designed. Unlike large vessels operating in open waters, small autonomous vehicles operating in complex underwater environment have to extract the sensory information they need from clutter echoes, i.e., echoes that consist of many contributions from unresolved scatterers and have hence to be treated as random waveforms for lack of knowledge. This sensing scenario is not unlike that of certain bat species that have mastered hunting their prey in dense vegetation based on biosonar echoes. All bats have a poor angular resolution due to their small sizes and comparatively long ultrasonic wavelengths and must hence be able to obtain all the sensory information that is required by their active, predatory lifestyles without resolving all the individual scatterers in their environments. Biomimetic robotics inspired by bats combined with deep learning offers hence a unique opportunity to revisit the problem of extracting sensory information from clutter echos without resolving the individual scatterers. Following this approach, the authors have created robots that mimic the periphery of bat biosonar systems, including their unique dynamics where the emitting (so-called "noseleaves") and receiving structures (i.e., outer ears) change their shapes while interacting with the outgoing and returning waveforms. Based on data sets consisting of biomimetic echoes from natural environments (esp. foliages), deep-learning methods successfully demonstrated that it is possible to detect narrow passageways and identify locations in natural environments without resolving the scatterers that have contributed to the echoes. For the passageway-finding problem, it was not only possible to detect narrow gaps reliably from a distance, but transparent AI methods (class-activation mapping) were able to pinpoint the rising flank of the echoes as the region that contained most of the relevant sensory information. Based on this insight, it was possible to create a neuromorphic spike code for the shape of the rising echo flank that performed almost as good as a deep neural network in detecting the gaps. The problem of identifying a location without GPS is particularly relevant for autonomous underwater navigation. Based on biomimetic sonar echoes, it has been possible to identify locations in a natural forest with a precision that was limited by the employed recreation-grade GPS reference. Finally, the impact of bats' biosonar peripheral dynamics on clutter echoes is currently under investigation.

Bats, Biomimetic Robots, Machine Learning, and Making Sense of Clutter Echoes

The lack of automation for model improvement, validation, and performance monitoring is a critical limitation on the delivery rate of AI/ML models and data products to the warfighter. Currently, the DoD fields machine learning models through traditional analysis, development, training and test processes akin to existing block cycle software update model, which can take as long as two years. In the age of software-defined systems and AI/ML driven behavior, when enemy hardware capabilities can be updated and radically altered within hours, the difference in delivery time creates a strategic vulnerability across many domains. Our research has focused on the ability to operationalize Machine Learning Operations (MLOps) at the edge, fog, and cloud with Infrastructure as Code (IaC) to accelerate delivery and provide a standard operating model in theater and for analysts back at home. With access to Electronic Warfare (EW) and flight test data analysis subject matter experts (SME), we have had the unique opportunity to create representative use cases to analyze the delivery time between data gathering, data labelling, model training, and model testing. 

Edge Deployable MLOps for Accelerated Model Delivery

The ability to detect and categorize very small unmanned aerial vehicles (UAV) is of crucial importance for situational awareness and defense of vulnerable assets. However, the ability to process timeseries data on man-portable hardware without impacting mobility is an ongoing issue in the machine learning community. We present a novel approach that leverages developments from modern neurophysiology to convert timeseries information to highly separable spectral representations, which are well suited to machine learning techniques. Specifically, we use multitaper spectral estimation (Thompson, 1982) to average spectral content over orthogonal tapers and across data-defined cyclostationary timescales. This results in a better estimate of the central spectral frequency of each bin, while simultaneously averaging out noise that is not physically related to the source. We demonstrate this technique by comparing naive networks operating on spectral data (both multi-layer perceptrons and convnets) with those operating on multitaper data. These networks are trained on experimentally acquired recording of very small consumer drones (DJI and Parrot, <5 kg gross weight) and show very high specificity and accuracy. We show that there is a statistically significant performance advantage for the multitaper method, and which holds under large training data restrictions (i.e. 90%). Our novel ML pipeline thus provides a suitable basis for continuously retrainable edge deployment of UAV detectors.

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Estimating Sound Speed Profiles Using Vessel Source Signals of Opportunity

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RippleGo - First AI-Based Voyage Planning and Predictive Analytics Software for the US Inland Waterways

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