25 August 2003 Bayesian cluster detection and tracking using a generalized Cheeseman approach
Author Affiliations +
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).
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ronald P. S. Mahler, 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); doi: 10.1117/12.492945; https://doi.org/10.1117/12.492945
PROCEEDINGS
12 PAGES


SHARE
Back to Top