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Ping Strategies for an Intelligent Search using Multistatic Active Sonar

Description:

RT&L FOCUS AREA(S): General Warfighting Requirements

TECHNOLOGY AREA(S): Battlespace Environments

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 ping strategies, in a simulation environment, that provide optimized performance for multistatic active sonar fields with a target that actively seeks to evade detection by the sonar field.

DESCRIPTION: One of the challenging components for developing new sonar systems and improvements to them is collecting data so that the system is mature with robust performance in a wide variety of acoustic environments.  Execution of data gathering events requires large investments funding Navy personnel and assets. In order to reduce costs while developing a system, the Navy seeks to employ models and simulations to the maximum extent possible reducing the need for a large number of data gathering events.

This SBIR topic seeks to develop foremost ping strategies and signal and information processing techniques to optimize search performance that can be validated against a realistic target motion model in a simulation environment.  Development of the target motion model is required and that model should include techniques for the target to avoid detection when located in a multistatic active coherent (MSAC) wide area search field.  Real-world parameters such as the sound speed profile and bathymetry will be provided. A reactive target model that seeks to evade an active multistatic field and remain undetected will enable more meaningful simulation results of the ping strategies under evaluation and will better demonstrate the effectiveness of the proposed changes.  Historical approaches to the detection problem [Ref 7] focus on reconciling the sonar equation. The Navy seeks to develop ping strategies that leverage signal and information processing or other techniques in addition to just reconciling the sonar equation that will improve the probability of detection and show an improvement against a reactive target model that is able to maneuver, change speed, and change depth. Because the target has mass  (i.e., the size of a manned platform), instantaneous changes in speed or direction should not be considered in the target motion model.

Work produced in Phase II may be 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 Counterintelligence and Security Agency (DCSA) formerly known as 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 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 IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Demonstrate the feasibility of ping strategies for a notional multistatic sonar system, which improves performance against an optimized reactive target model. Show that these new strategies improve performance versus a random ping schedule. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Develop and implement ping strategies for a simulated MSAC field with a reactive target including broadband and narrowband waveforms, multiple input multiple output (MIMO) pinging, and high-duty cycle (HDC) pinging. Demonstrate that new ping strategies can successfully detect a reactive target 25% more often than simple ping schedules.

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: Finalize and implement the capability as part of an operational sonar system. Transition of this capability should utilize the Advanced Product Builds (APB) process. The search techniques developed under this effort have application across the Navy for sonar, radar, electro-optic, and other sensor devices.

The searching or tracking of mobile targets where the sensors are stationary would benefit from this capability (i.e., tracking assets in an urban battlefield). A potential commercial application would be to the gaming industry especially if the object of the game was to avoid detection or capture.

REFERENCES:

  1. Jackson, P. “Introduction to Expert Systems (3rd ed.).” Addison Wesley, 1998. ISBN 978-0-201-87686-4. http://www.pearsoned.co.uk  
  2. Pike, J. and Sherman, R. “Run Silent, Run Deep.” Federation of American Scientists, December 8, 1998. https://fas.org/man/dod-101/sys/ship/deep.htm  
  3. Gilliam, C.; Angley, D.; Williams, S.; Ristic, B.; Moran, B.; Fletcher, F. and Simakov, Sergey. “Covariance Cost Functions for Scheduling Multistatic Sonobuoy Fields [Paper presentation].” International Conference on Information Fusion, Cambridge, UK, July 10-13, 2018. https://www.researchgate.net/publication/325597536_Covariance_Cost_Functions_for_Scheduling_Multistatic_Sonobuoy_Fields  
  4. Kaelbling, L.P.; Littman, M.L. and Moore, A.W. “Reinforcement Learning: A Survey.” Journal of Artificial Intelligence Research, 4, May 1, 1996, pp. 237-285. https://doi.org/10.1613/jair.301  
  5. Fran├žois-Lavet, V.; Henderson, P.; Islam, R.; Bellemare, M.. and Pineau, J. “An Introduction to Deep Reinforcement Learning.” Foundations and Trends in Machine Learning, 11(3–4), December 20, 2018, pp. 219-354. https://doi.org/10.1561/2200000071  
  6. DoD 5220.00-M: National Industrial Security Program Operating Manual. Department of Defense, February 28, 2006. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodm/522022m.pdf  
  7. Urick, R.J. “Principles of Underwater Sound (3rd ed.).” Peninsula, 2013. ISBN 9780932146625. https://peninsulapublishing.com/product/principles/
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