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Expectation-Maximization Algorithm for Radar Centroid Processing with Tracker Feedback (EMARCT)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-07-C-0125
Agency Tracking Number: N052-118-0494
Amount: $750,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N05-118
Solicitation Number: 2005.2
Timeline
Solicitation Year: 2005
Award Year: 2007
Award Start Date (Proposal Award Date): 2007-05-03
Award End Date (Contract End Date): 2009-05-03
Small Business Information
40 Lloyd Avenue, Suite 200
Malvern, PA 19355
United States
DUNS: 075485425
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 C. Allen Butler
 Vice President
 (757) 727-7700
 Allen.Butler@va.wagner.com
Business Contact
 W. Monach
Title: Vice President
Phone: (757) 727-7700
Email: reynolds@va.wagner.com
Research Institution
N/A
Abstract

The raw detection data obtained by a radar typically consists of primitive measurements that correspond to discrete range-bearing cells, multiple of which can contain energy reflected from a single target. The radar system employs a centroid processing algorithm to combine the primitive measurements and form a single detection report. However, if multiple targets exist close to one another, this centroid process can produce undesirable results such as track degradation and/or track drop, since the measurements for more than one target may be combined to produce a single report. We propose a method for utilizing feedback from the tracker to produce the appropriate number of centroid estimates in a given area, thus avoiding track drop. Our method is a modification of the Expectation-Maximization algorithm as applied to the Gaussian Mixture Estimation problem; accordingly, we will refer to our algorithm as EMARCT (an Expectation Maximization Algorithm for Radar Centroid processing with Tracker feedback). In Phase II, we will enhance the algorithm by improving the initialization and termination components of the iteration and by adding the ability to independently estimate the number of targets. We will program the algorithm in C++ and demonstrate its feasibility on simulated and real-world data.

* Information listed above is at the time of submission. *

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