N251-D01 TITLE: DIRECT TO PHASE II: Passive Acoustics Sonobuoys Intelligence and Machine Learning

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Human-Machine Interfaces;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 an efficient, robust, and automated system that can process passive acoustic signals, effectively reducing human intervention, reducing operator workload, and improving data analysis accuracy. Develop and demonstrate innovative technologies, including new techniques from other signal processing domains, to enable the Directional Frequency Analysis and Recording (DIFAR) sonobuoy and Airborne Anti-Submarine Warfare (ASW) systems to automatically and accurately detect, classify, track, and localize threats.

DESCRIPTION: The Air Anti-Submarine Warfare (ASW) Systems Program Office (PMA-264), in response to the evolving challenges posed by nation states with significant investment in undersea capability and capacity, wants to develop innovative technologies that advance air ASW systems to reliably detect, classify, track, and localize submarines and unmanned underwater vehicles (UUVs) via passive sensors. This SBIR effort is to be confined to the acoustic processor on an aircraft (manned or unmanned). This SBIR effort should not require any changes to the sonobuoy. The focus is to incorporate advanced technology solutions with integrated sensor data to speed up reaction times that support the acoustic operator and improve passive acoustic target detection, classification, tracking, and localization.

Advanced technologies have demonstrated promise in other signal processing domains such as target discrimination, the development of matched filters or templates for never-before-seen targets, and incoherent aggregation of signatures. Techniques may include generating unique identifiable signatures by combining data from multiple receivers or receptions in highly dynamic, real-time environments— even on computationally limited assets—to aid in automated detection, classification, tracking, and localization. These techniques produce additional metadata surrounding a signature to aid or refine classification. Recognition accuracy, reliability, and speed of processing are all important performance goals. The automated and accurate detection, classification, tracking, and localization of targets will go a long way in maintaining ASW superiority in the theater.

Recent advances in artificial intelligence (AI) sensing of passive acoustic data have paved the way for further improvements that rely more on automated processing of data with human operator oversight. The goal is to increase automation in passive acoustic processing with sensor information on the platform and ensure that other relevant data is being leveraged to better inform decision-making (e.g., environmental data, historical data, and sensor fusion data). Solutions will need to show significant accuracy in the detection, classification, tracking, and localization of undersea contacts.

Although the topic chiefly requires a technical solution, it also is focused on developing and maturing the continuum of human-machine interaction. The envisioned solution would allow operators to be part of the process sometimes, with the capability providing them decision-support. While at other times allowing operators to be supervisors of the analysis, providing oversight and expertise. This topic requires a level of automation transparency with the operators to enable a human to quickly understand why the system is presenting the information including the basis of automated detection, classification, and localization. The importance of the human operator’s ability to adapt to new information, answer questions about information, and truly analyze an ASW target’s characteristics and behavior cannot be overstated. The proposed capability needs to support the warfighter in his or her job and be a power multiplier, while reducing operator workload.

Multiple passive sonobuoys can enable the rapid identification, classification, and localization of threats. In order to extend the current capabilities, efficient innovative techniques are needed. The proposed solution should be designed to significantly enhance the efficiency of detecting, classifying, tracking, and localizing underwater threats, outperforming current capabilities managed by human operators. The system aims to speed up the detection and classification processes, ensuring a high degree of accuracy and reliability. Throughout the development effort, the proposer will be required to provide clear and provable metrics that demonstrate performance improvements when compared to the average acoustic operator capability.

The proposed solution must meet ethics principles outlined by the Department of Defense (Responsible, Equitable, Traceable, Reliable, and Governable) and present a plan that will be accounted for and tracked throughout the effort. Additionally, the final solution will meet Risk Management Framework and Cyber Security.

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 NAVAIR 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: For a Direct to Phase II topic, the Government expects that the small business would have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The below actions would be required to satisfy the requirements of Phase I:

Demonstrate the proof-of-concept techniques for advanced identification, data fusion, signal aggregation, and classification of targets in dynamic ASW scenarios. Demonstrate advanced passive sonar techniques that significantly improve target discrimination in tactical scenarios.

FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic NOT solely based on work performed under prior or ongoing federally funded SBIR/STTR work) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above.

PHASE II: Develop a working prototype that can incorporate the developed advanced passive sonar processing techniques. The prototype capability should be able to ingest synthetic data, as well as real-world training data. Demonstration of the prototype capability should include its capability to perform autonomously and independently, as well as with an operator working with the system.

Work in Phase II may become classified. Please see note in Description paragraph.

PHASE III DUAL USE APPLICATIONS: Refine the capability from the Phase II final demonstration and show consistent reliability in a known performance envelope. A Phase III capability must include and demonstrate a function to self-tune its own algorithm based on new data inputs. The Phase III system must integrate engineering and pass operationally representative testing on an air deployed system and support Navy-supported test scenarios within current acoustic warfare operator training and go through verification and validation testing, as well as effectiveness and usability testing. Transition completed technology to fleet or appropriate Navy platform.

Technology developed in this SBIR topic could be leveraged for other marine monitoring applications. This technology could include air-deployable search and rescue hardware; resource exploration sensor technology; and oceanographic survey instrumentation.

REFERENCES:

1. Verburgt, P. W. "Introduction to the theme: Airborne anti-submarine warfare." U.S. Navy Journal of Underwater Acoustics, 62(3), June 2014. https://apps.dtic.mil/sti/pdfs/ADA610348.pdf

2. Holler, R. A.; Horbach, A. W. and McEachern, J. F. "The ears of air ASW: a history of US Navy sonobuoys." Navmar Applied Sciences Corporation, 2008. https://www.worldcat.org/title/720627294

3. Saffari, A.; Zahiri, S. H. and Khishe, M. "Automatic recognition of sonar targets using feature selection in micro-Doppler signature." Defence Technology, 20, 2023, pp. 58-71. https://doi.org/10.1016/j.dt.2022.05.007

4. Azimi-Sadjadi, M. R.; Wilbur, J. and Dobeck, G. J. "Isolation of resonance in acoustic backscatter from elastic targets using adaptive estimation schemes." IEEE journal of oceanic engineering, 20(4), 1995, pp. 346-353. https://ieeexplore.ieee.org/document/480597

5. Wang, P. and Peng, Y. "Research on feature extraction and recognition method of underwater acoustic target based on deep convolutional network." 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), August 2020, pp. 863-868. https://ieeexplore.ieee.org/document/9213504

6. Fischell, E. M. and Schmidt, H. "Multistatic acoustic characterization of seabed targets." The Journal of the Acoustical Society of America, 142(3), 2017, pp. 1587-1596. https://asa.scitation.org/doi/10.1121/1.5002887

7. "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: Automation; Autonomy; Anti-Submarine Warfare; Machine Learning; Artificial Intelligence; Explainability

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