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Proceedings Paper

Cost-function-based hypothesis control techniques for multiple hypothesis tracking
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Paper Abstract

The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. Modern tracking methods maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on simple merging and pruning rules to control the growth of hypotheses. This paper proposes a structured, cost-function-based approach to the hypothesis control problem, utilizing the Integral Square Error (ISE) cost measure. A comparison of track life performance versus computational cost is made between the ISE-based filter and previously proposed approximations including simple pruning, Singer's n-scan memory filter, Salmond's joining filter, and Chen and Liu's Mixture Kalman Filter (MKF). The results demonstrate that the ISE-based mixture reduction algorithm provides track life performance which is significantly better than the compared techniques using similar numbers of mixture components, and performance competitive with the compared algorithms for similar mean computation times.

Paper Details

Date Published: 25 August 2004
PDF: 13 pages
Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.542325
Show Author Affiliations
Jason L. Williams, Air Force Institute of Technology (United States)
Massachusetts Institute of Technology (United States)
Peter S. Maybeck, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 5428:
Signal and Data Processing of Small Targets 2004
Oliver E. Drummond, Editor(s)

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