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Proceedings Paper

Signature-aided tracking using association hypotheses
Author(s): Craig S. Agate; Kevin J. Sullivan
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Paper Abstract

An algorithm is derived for signature-aided tracking which uses features (e.g. high-range resolution radar (HRRR) profiles), or functions of features, in addition to kinematic measurements to associate measurements to known tracks, clutter or new tracks. The approach taken here is to derive the probability of the measurement-to-track association hypotheses which incorporates the likelihood of features as well as the traditional approach of using the kinematic measurement likelihood. It is assumed that the probability density function (PDF) of the features (or some function of the features) is available from a library. The approach to probabilistically characterizing the PDF of the profiles relies on the availability of a class-specific library for each target type. The class-specific library of PDFs characterizes the profiles conditioned on the target class from which the profile originated and the aspect at which the profile was obtained. The algorithm is evaluated using the SLAMEM simulation.

Paper Details

Date Published: 31 July 2002
PDF: 12 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477625
Show Author Affiliations
Craig S. Agate, Toyon Research Corp. (United States)
Kevin J. Sullivan, Toyon Research Corp. (United States)

Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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