A Novel and Integrated Framework for Sensor Registration and Sensor Management

Award Information
Agency:
Department of Defense
Amount:
$100,000.00
Program:
SBIR
Contract:
HQ0006-09-C-7164
Solitcitation Year:
2008
Solicitation Number:
2008.3
Branch:
Missile Defense Agency
Award Year:
2009
Phase:
Phase I
Agency Tracking Number:
B083-040-0199
Solicitation Topic Code:
MDA08-040
Small Business Information
SIGNAL PROCESSING, INC.
13619 Valley Oak Circle, ROCKVILLE, MD, 20850
Hubzone Owned:
N
Woman Owned:
Y
Socially and Economically Disadvantaged:
Y
Duns:
620282256
Principal Investigator
 Chiman Kwan
 Chief Technology Officer
 (240) 505-2641
 chiman.kwan@signalpro.net
Business Contact
 Chihwa Yung
Title: President
Phone: (301) 315-2322
Email: chihwa.yung@signalpro.net
Research Institution
N/A
Abstract
In the registration part, the goal is to eliminate sensor biases and yield an accurate and integrated picture of the target tracks. In the sensor management part, the goal is to use a limited number of sensors to achieve certain tasks (e.g., accurate fire control) without using computationally expensive methods. We propose a highly innovative approach to sensor registration and sensor management. Our architecture can deal with disparate sensors. In the sensor registration part, the first step is to initiate the tracks using sensor measurements. We propose a recursive nonlinear prediction approach to perform this initialization. This predictor is robust to background clutter. Then we propose a track correlation technique called sequential minimum normalized distance nearest neighbor (SMNDNN) method. We have shown that only those track pairs which satisfy the minimum normalized distance will remain, and the maximum possible assignment is arrived at under the condition that each track is correlated only once. Finally, we apply Expectation-Maximization-Extended Kalman Filter (EM-EKF) to yield combined target tracks. In the sensor management part, we propose to apply recently developed sensor management algorithms known as covariance control, which has been proven to be very useful in estimating interacting (close) target tracks using a small number of sensors. The computational burden is also reasonable.

* information listed above is at the time of submission.

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