Submarine Sensor Environmental Inference
Navy SBIR 2018.2 - Topic N182-135
ONR - Ms. Lore-Anne Ponirakis -
Opens: May 22, 2018 - Closes: June 20, 2018 (8:00 PM ET)


TITLE: Submarine Sensor Environmental Inference


TECHNOLOGY AREA(S): Battlespace, Information Systems, Sensors

ACQUISITION PROGRAM: PEO Integrated Warfare Systems, Advanced Processor Build (APB), Non-ACAT

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 section 3.5 of 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 environmental inference capabilities to provide in situ characterizations of the speed and attenuation of sound in the seabed and water column to enhance the tactical decisions and warfighting posture of submarines so informed.

DESCRIPTION: In light of an increasingly competitive undersea operational arena, the Navy is in need of improved environmental situational awareness, and in particular sound speed profile (SSP) and bottom properties that affect the performance of submarine sonar sensors. Current approaches rely heavily on databases and remote ocean models to provide descriptions of the environment. The data base and model information can be compared to and in some cases coupled with an in situ measurement using a submarine expendable bathythermograph (SSXBT) that measures temperature and depth, but the available communication bandwidth limits the ability to push high-granularity model data forward and the cost and effort of SSXBT launch limits the practical update rate from that source. Inversion, data assimilation, and artificial intelligence methods that are able to fuse the traditional sources of data with local measurements like seawater injection temperature and “matched field” information derived from the sonar systems are needed to improve the currency of the environmental picture and provide measures of uncertainty for the picture so obtained.

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 DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and the Office of Naval Research (ONR) 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 IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Develop a framework for exploiting existing in situ data from sonar systems coupled with other measurements and legacy environmental support products to produce a multi-source inference of the submarine’s surroundings. Analyze and specify the sonar or other data requirements necessary to develop and support the determination and representation of the multi-source inference and its uncertainty. Develop a Phase II plan.

PHASE II: Using operational, research and development (R&D), academic, or other measurement data, refine the methodology and conduct proof-of-concept demonstrations and tests of the multi-source inference algorithm and the impact of the increased skill on the operation of a candidate sonar system. Develop partnerships with Program Executive Office Integrated Warfare Systems Undersea Systems (PEO IWS-5) and other stakeholders in development of an Advanced Processer Build (APB) embedded tactical decision aid.

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: Transition the resulting algorithm through the Advanced Processing Build (APB) tactical software development program that is designed to improve Navy submarine acoustic performance by taking full advantage of commercial processing hardware. The APB process will couple suitable algorithms developed under this SBIR topic with Prime Contractor engineering, integration, and testing to transition the enhanced capability to the fleet.

It is possible that a proposer and subsequent performer would look to and end up employing Artificial Intelligence (AI) techniques, like Deep Learning, to tackle and exploit the large data sets that would be the basis environmental inference. While there would not be dual use applications in civilian/commercial markets for the environmental inference developments, the proposer might use the AI experience gained in this effort to tackle a very different application in commercial markets.


1. Baggeroer, A. B., Kuperman, W. A., and Mikhalevsky, P. N. “An overview of matched field methods in ocean acoustics.” IEEE J. Oceanic Eng. 18, 1993, p. 401.

2. Siderius, M., Harrison, C. H., and Porter, M. B. “A passive fathometer technique for imaging seabed layering using ambient noise.” The Journal of the Acoustical Society of America 120, 2006, pp. 1315-1323.

3. Gemba, K. L., Hodgkiss, W. S., and Gerstoft, P. “Adaptive and compressive matched field processing.” The Journal of the Acoustical Society of America 141, 2017, p. 92.

4. Thomas, Adam J. “Tri-Level Optimization For Anti-Submarine Warfare Mission Planning.” M.S. thesis in Operations Research, Naval Postgraduate School, Monterey, CA., 2008

KEYWORDS: Ocean; Acoustic; Inference; Bayesian; Machine Learning; Environment; Matched Field


Robert Headrick




Kyle Becker




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