N251-039 TITLE: Physics-based Data Augmentation for Machine Learning (ML) Models

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software;Trusted AI and Autonomy

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop a tool to synthesize realistic physics-based sonar data for use in training Artificial Intelligence/Machine Learning (AI/ML) algorithms to enable rapid approaches to fielding sonar-oriented AI/ML capabilities.

DESCRIPTION: For imagery and vocal audio, tools exist to allow individuals to generate realistic audio and video clips for speeches, also known as deep fakes. These tools use a variety of AI/ML tools and limited exemplars of training data.

For sonar, there are tools to compute representative acoustics on sonar arrays to support sailor training objectives. Recording data at sea is currently used to obtain training data for sonar signal processing and it is cost prohibitive to obtain the quantity of data required to train AI/ML algorithms. The complex, physics-based models used in current simulations require a fundamental understanding of the entire phenomenon in question and requires extreme computational power. Data-generation tools exist in industry. However, these tools are not oriented toward sonar and existing tools are not sufficient to develop dynamic scene content covering 360 degrees at extended ranges to support mid-frequency sonar (1 kHz to 10 kHz) across the worldwide range of bathymetric, weather, volume scattering, and contact density conditions. Innovation is required to support the generation of phenomenologically representative data sets. The Navy seeks a tool to synthesize realistic physics-based sonar data for use in training AI/ML algorithms to enable rapid approaches to fielding sonar-oriented AI/ML capabilities. Currently, there are no commercial tools that can do this.

Success with video and vocal audio generation using AI/ML tools suggests that it may be possible to combine recorded exemplars obtained during exercises such as Rim of the Pacific (RIMPAC) with physics-based contact attributes to generate high quality sonar data. The primary use for this generated data would be to train emerging AI/ML algorithms.

AI/ML synthesis tools can enable development of realistic synthetic sonar data for use in training AI/ML algorithms. A limiting factor is the availability of recorded training data and the absence of recorded data from real-world conflict situations involving realistic numbers of enemy contacts. High-quality synthesis approaches that utilize AI/ML would provide an alternate means to creating the large volumes of data needed to train detection and classification algorithms.

The solution must include using generative adversarial models and deep predictive coding models. It must be capable of producing large volumes of diverse high-fidelity data to train ML algorithms that will improve target detection, classification, and tracking systems. Metrics for the solution includes computational performance, "image" similarity metrics, and user assessments.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: Develop a concept for a tool to produce realistic synthetic sonar sequences suitable for training signal processing algorithms that meet the feasibility of parameters in the Description. Feasibility will be established through modeling and analysis of the design.

The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and deliver a prototype tool of the realistic synthetic sonar sequences. Demonstrate the tool’s ability to meet the parameters in the Description through testing. Testing will include benchmarking computational performance, "image" similarity metrics compared to recorded sonar exemplars (which will be provided by the government), and user assessments. Validate the prototype through application of the approach for use in a simulation environment. Provide a detailed test plan to demonstrate that the simulation achieves the metrics defined in the Description.

Due to the nature of recorded sonar data, it is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the tool to Navy use in training current Navy sonar signal processing algorithms as well as with training systems or simulators. Work with the IWS 5.0 Undersea Systems program working groups for ML and training to increase the fidelity of the sonar sensor data used for training AI/ML algorithms and used within high fidelity sonar trainers.

The technology developed under this SBIR topic could provide an improved approach to creating dynamic scene content for other DoD programs. If this AI/ML-generated sonar data can be generated with less computational power than current physics-based models, this technology may also be of use in trainers for sailors.

Complex, physics-based models are often used in current simulations. This requires a fundamental understanding of the entire phenomenon in question and requires extreme computational power.

The innovation sought would reduce reliance processing capacity while retaining traceability to physical attributes of sonar returns. This new approach could be used for sensor data prediction and interpolation for scenarios where it is not possible to record data (e.g., wartime conflict situations) or to produce sonar data to train for salvage operations, oil and gas exploration, and border protection.

REFERENCES:

1. Tiu, E.; Talius, E.; Patel, P. et al. "Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning." Nat. Biomed. Eng 6, 2022, pp. 1399-1406 (2022). https://doi.org/10.1038/s41551-022-00936-9

2. Wang, Yaqing; Yao, Quanming; Kwok, James T. and Ni, Lionel M. "Generalizing from a Few Examples: A Survey on Few-shot Learning." ACM Comput. Surveys, 53(3), 2020. https://www.bing.com/ck/a?!&&p=6a3a95f1d66b22afJmltdHM9MTcxMjUzNDQwMCZpZ3VpZD0xMjM1NTcyYy0xNGQ2LTY4NzctMjU1My00Mzc3MTU0MjY5NDImaW5zaWQ9NTIyMg&ptn=3&ver=2&hsh=3&fclid=1235572c-14d6-6877-2553-437715426942&psq=2.+Wang%2c+Yaqing%2c+Yao%2c+Quanming%2c+Kwok%2c+James+T.+and+Ni%2c+Lionel+M.+(2020).+Generalizing+from+a+Few+Examples%3a+A+Survey+on+Few-shot+Learning.+ACM+Comput.+Surveys%2c+53(3)&u=a1aHR0cHM6Ly9hcnhpdi5vcmcvYWJzLzE5MDQuMDUwNDY&ntb=1

3. "AN/SQQ-89(V) Undersea Warfare / Anti-Submarine Warfare Combat System, updated 20 Sep 2021." https://www.navy.mil/Resources/Fact-Files/Display-FactFiles/Article/2166784/ansqq-89v-undersea-warfare-anti-submarine-warfare-combat-system/

4. "The Essential Guide to Quality Training Data for Machine Learning: What You Need to Know About Data Quality and Training the Machine." Cloudfactory. https://www.cloudfactory.com/training-data-guide

5. Rim of the Pacific (RIMPAC) international maritime exercise website. https://www.cpf.navy.mil/RIMPAC/

6. "National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. (1993)." https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

 

KEYWORDS: Train emerging AI/ML algorithms; AI/ML synthesis tools; High-quality synthesis approaches that utilize AI/ML; mid-frequency sonar; deep predictive coding models; physics-based contact attributes

TPOC 1: Meg Stout
(202) 781-4233
Email: [email protected]

TPOC 2: Stephan Shomberger
(202) 781-269
Email: [email protected]

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