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Feature-Aided RF/IR Track Correlation

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7187
Agency Tracking Number: B12A-001-0053
Amount: $99,986.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA12-T001
Solicitation Number: 2012.A
Timeline
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2012-11-13
Award End Date (Contract End Date): 2013-05-13
Small Business Information
MA
Andover, MA 01810-1077
United States
DUNS: 073800062
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Andrew Stachyra
 Principal Research Scientist
 (978) 689-0003
 astachyra@psicorp.com
Business Contact
 B. Green
Title: President and CEO
Phone: (978) 689-0003
Email: green@psicorp.com
Research Institution
 Johns Hopkins University
 Timothy Galpin
 
Applied Physics Laboratory 11100 Johns Hopkins Road
Laurel, MD 20723-6099
United States

 (242) 228-1141
 Nonprofit College or University
Abstract

Missile tracking sensors such as radar and EO/IR are often called upon to track constellations of multiple closely spaced objects, and recognize pairs of matching tracks from each sensor"s view. The outcome of this track correlation process is crucial for missile defense interceptors, as any failure to identify matches with certainty, for example because of an excessive number of tracks associated with debris, will likely mean that information gathered prior to the interceptor"s own acquisition of the scene can"t be fused reliably and will effectively be rendered useless. The current practice in the Aegis BMD system is for the radar to deliver a target object map (TOM) message to the interceptor, which the interceptor matches against the EO/IR tracks in its own field of view using a distance-based likelihood metric. However, both the RF and EO/IR sensors also have time series signature data available from the objects they are tracking. Physical Sciences Inc. (PSI) and Johns Hopkins University Applied Physics Laboratory (JHU/APL) propose to incorporate this additional information by reducing the signature data to a small set of characteristic features, and then combine the resulting feature values with existing distance-based metrics to produce an augmented track correlation solution.

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

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