TECHNOLOGY AREAS: Information Systems, Ground/Sea Vehicles, Sensors

ACQUISITION PROGRAM: PEO(LMW) / PMS-495, applicable to all underwater sonars and electro-optics

OBJECTIVE: Develop compressive sensing algorithms and techniques for underwater tactical applications. The goal is to reduce the sampling requirements resulting in higher area coverage rates, reduced datasets, and / or relaxed sensing geometry requirements. Sensors of interest include synthetic aperture sonar, real aperture sonar, electro-optics, and magnetic.

DESCRIPTION: The task of sensing & recognizing objects underwater often requires large amounts of data to be sampled relative to the signal of interest (e.g., submarine detection, electro-optic mine detection, etc). In other applications highly sophisticated and computationally expensive techniques for estimating sensing geometry are required (e.g., motion compensation for synthetic aperture sonar). Compressive sensing techniques are relatively new yet they possess a rich and principled mathematical foundation. They enable the simultaneous sensing and compression of sparse signals traditionally acquired with substantial oversampling while also exploiting the potential random variations in sampling geometry. Therefore, ONR is seeking the development of compressive sensing algorithms for tactical application in the underwater environment. The proposed algorithms may work to reduce the sampling requirements or may simultaneously sense and recognize targets depending on the proposer’s area of expertise. Example datasets may be provided if required.

PHASE I: Development of overall concept, detailed description of how sensing will be performed, and proof-of-concept algorithms demonstrated on test data (government provided if required).

PHASE II: Extend proof-of-concept algorithms from Phase I to robustly perform in a laboratory environment given a government provided dataset. This will require an investigation into bounds on performance (e.g., requirements on aspect / sensing geometry and sampling matrix for sufficient reconstruction or recognition).

PHASE III: Extend algorithms to be robust and fault tolerant to a full spectrum, government provided dataset. Work with government labs and other contractors to integrate into a sensing platform and participate in at-sea testing.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: These techniques are applicable to all industries requiring underwater surveys, searches, or mapping including petroleum, utilities, and geology.

REFERENCES:
1. R. Baraniuk, J. Romberg, and M. Wakin, "Tutorial on compressive sensing" Information Theory and Apps Workshop, available: http://www.dsp.ece.rice.edu/cs/, 2008.

2. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans Information Theory, vol. 52, no 2, pp. 489-509, Feb. 2006.

3. D. Donoho, "Compressed sensing," IEEE Trans Information Theory, vol. 52 no. 4, pp. 1289-1306, Apr. 2006.

KEYWORDS: Automatic target recognition; autonomous systems; classification; compressive sensing; compressed sensing; sampling