Exploiting Multipath for Efficient Target Classification

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-10-C-0519
Agency Tracking Number: N102-139-0180
Amount: $79,796.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: N102-139
Solicitation Number: 2010.2
Small Business Information
Signal Innovations Group, Inc.
1009 Slater Rd., Suite 200, Durham, NC, 27703
DUNS: 147201342
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 James Baxter
 Engineer 4
 (919) 323-4813
 jbaxter@siginnovations.com
Business Contact
 Samantha Venters
Title: VP Finance
Phone: (919) 323-3453
Email: sventers@siginnovations.com
Research Institution
N/A
Abstract
Automatic target recognition(ATR) for tactical military targets is a very challenging problem, particularly when the number of observed aspects of a target is subject to the operational constraints of the sensor platform. When the target is situated in the presence of a complicated background medium, such as in urban or hilly settings, the observed signals might be deformed significantly, thus the classification performance suffers. However, it can be proved that the observed signals of a target in the presence of a complicated environment are linear combination of the target’s scattering fields situated in free space. Thus the target is actually observed from multiple aspects for each such measurement. On the other hand, the angle-dependent target responses are typically a smooth function of aspect angles, and therefore are highly compressible. In this Phase I, the concept of in situ compressive sensing (CS) will be employed to exploit the multipath response. ATR can subsequently be performed with a relatively smaller number of observations within compact aspect range. The target RCS/far fields in free space will be simulated via multilevel fast multipole algorithm (MLFMA), and various random projection matrices will be generated to simulate simple and complicated environments, respectively. Sparse Bayesian classifiers will be used to assess the classification performance and efficiency of CS compared with conventional approaches.

* information listed above is at the time of submission.

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