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Exploiting Multipath for Efficient Target Classification

Award Information

Agency:
Department of Defense
Branch:
Navy
Award ID:
98649
Program Year/Program:
2010 / SBIR
Agency Tracking Number:
N102-139-0180
Solicitation Year:
N/A
Solicitation Topic Code:
NAVY 10-139
Solicitation Number:
N/A
Small Business Information
Signal Innovations Group, Inc.
4721 Emperor Blvd. Suite 330 Durham, NC -
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2010
Title: Exploiting Multipath for Efficient Target Classification
Agency / Branch: DOD / NAVY
Contract: N68335-10-C-0519
Award Amount: $79,796.00
 

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.

Principal Investigator:

James Baxter
Engineer 4
9193234813
jbaxter@siginnovations.com

Business Contact:

Samantha Venters
VP Finance
9193233453
sventers@siginnovations.com
Small Business Information at Submission:

Signal Innovations Group, Inc.
1009 Slater Rd. Suite 200 Durham, NC 27703

EIN/Tax ID: 201104360
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No