Bayesian cluster detection and tracking using a generalized Cheeseman approach

Ronald P. S. Mahler

doi:10.1117/12.492945

25 August 2003 Bayesian cluster detection and tracking using a generalized Cheeseman approach

Ronald P. S. Mahler

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.492945
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Cluster tracking is the problem of detecting and tracking clustered formations of large numbers of targets, without necessarily being obligated to track each and every individual target. We address this problem by generalizing to the dynamic case a static Bayesian finite-mixture data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's approach, we show that it implicitly draws on random set theory. Making this connection explicit allows us to incorporate it into a multitarget recursive Bayes filter, thereby leading to a rigorous Bayesian foundation for finite-mixture cluster tracking. A computational approach is proposed, based on an approximate, multitarget first-order moment filter (“cluster PHD” filter).

Citation Download Citation

Ronald P. S. Mahler "Bayesian cluster detection and tracking using a generalized Cheeseman approach", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.492945

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