Physical Model-Based Target Discrimination in Clutter using Surface-Based Radar

Award Information
Department of Defense
Missile Defense Agency
Award Year:
Phase I
Agency Tracking Number:
Solicitation Year:
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Small Business Information
6 New England Executive Park, Burlington, MA, 01803
Hubzone Owned:
Minority Owned:
Woman Owned:
Principal Investigator:
William Snyder
Lead Research Engineer
(781) 273-3388
Business Contact:
John Barry
Contracts Manager
(781) 273-3388
Research Institution:
With the appropriate processing algorithms, surface-based radar has the potential to discriminate reentry vehicles from other threat objects in the presence of clutter, which is a critical part of the BMD problem. Discrimination is based on resolving theobjects in range, and characterizing each object's shape and motion. The algorithm we propose is the model-based approach, which applies a hypothesize-and-test loop to compare radar data with a physical model of the data. In this approach, featuresextracted from the collected radar data are matched with features predicted from a series of scene hypotheses. The predictions use a physical model of the scene objects, including motion and shape, and run this through a signature prediction code. Thehypothesis search proceeds until an optimal shape and motion match for each object is found. Our other applications indicate that the model-based approach will offer several distinct advantages over rule-based and template methods, such as robustperformance in clutter. In this SBIR, we propose to develop algorithms to extract radar features and compare them with match metrics that perform well in the presence of countermeasures. We will then evaluate the discrimination performance of themodel-based approach with simulated clutter scenarios in Phase I. The technology developed under this program will contribute directly to the MDA objective of improving threat object classification from radar data for a variety of targets and under avariety of conditions. Specifically, the model-based approach will improve discrimination and kinematic parameter estimation of threat objects in the presence of countermeasures such as chaff. Furthermore, we anticipate that the development of themodel-based methods in this SBIR will provide advanced capabilities for commercial and law enforcement applications in high noise environments such as communications, surveillance, industrial and medical image processing, and civilian radar systems.

* information listed above is at the time of submission.

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