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Proceedings Paper

Multiple-frame multiple-hypothesis method for tracking at low SNR
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Paper Abstract

This paper develops a multiple-frame multiple-hypothesis tracking (MF-MHT) method and applies it to the problem of maintaining track on a single moving target from dim images of the target scene. From measurements collected over several frames, the MF-MHT method generates multiple hypotheses concerning the trajectory of the target. Taken together, these hypotheses provide a smoothed and reliable estimate of the target state. This work supports TENET, an Air Force Research Lab. Project that is developing nonlinear estimation techniques for tracing. TENET software was used to simulate both target dynamics and sensor measurements over a series of Monte Carlo experiments conducted at various signal-to-noise ratios (SNRs). Results are presented that compare computational complexity and accuracy of MF-MHT to two previously-documented nonlinear approaches to predetection tracking, a finite difference scheme and a particle filter method. Results show that MF-MHT requires about 2-3 dB more SNR to compete with the nonlinear methods on an equal footing.

Paper Details

Date Published: 31 July 2002
PDF: 9 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477620
Show Author Affiliations
John Greenewald, Veridian Systems, Inc. (United States)
Stanton Musick, Air Force Research Lab. (United States)

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

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