TY - GEN
T1 - The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking
AU - Carmi, Avishy
AU - Septier, François
AU - Godsill, Simon J.
N1 - Funding Information:
This work was sponsored by the Data and Information Fusion Defense Technology Centre, UK, under the Tracking Cluster. The authors thank these parties for funding this work.
PY - 2009/11/18
Y1 - 2009/11/18
N2 - We present a new filtering algorithm for tracking multiple clusters of coordinated targets. Based on a Markov Chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. Following our previous work, we adopt here two strategies for increasing the sampling efficiency of the basic MCMC scheme: an evolutionary stage which allows improved exploration of the sample space, and an EM-based method for making optimized proposals based on the frame likelihood. The algorithm's performance is assessed and demonstrated in both synthetic and real tracking scenarios.
AB - We present a new filtering algorithm for tracking multiple clusters of coordinated targets. Based on a Markov Chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. Following our previous work, we adopt here two strategies for increasing the sampling efficiency of the basic MCMC scheme: an evolutionary stage which allows improved exploration of the sample space, and an EM-based method for making optimized proposals based on the frame likelihood. The algorithm's performance is assessed and demonstrated in both synthetic and real tracking scenarios.
KW - EM algorithm
KW - Evolutionary MCMC
KW - Markov chain Monte Carlo filtering
KW - Multiple cluster tracking
UR - http://www.scopus.com/inward/record.url?scp=70449389007&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:70449389007
SN - 9780982443804
T3 - 2009 12th International Conference on Information Fusion, FUSION 2009
SP - 1179
EP - 1186
BT - 2009 12th International Conference on Information Fusion, FUSION 2009
T2 - 2009 12th International Conference on Information Fusion, FUSION 2009
Y2 - 6 July 2009 through 9 July 2009
ER -