TY - GEN
T1 - Evolutionary MCMC particle filtering for target cluster tracking
AU - Carmi, Avishy
AU - Godsill, Simon J.
AU - Septier, Francois
PY - 2009/4/8
Y1 - 2009/4/8
N2 - A new filtering algorithm is presented for tracking multiple clusters of coordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithm maintains a discrete approximation of the filtering density of the clusters' state. The filter's tracking efficiency is enhanced by incorporating two stages into the basic Metropolis-Hastings sampling scheme: 1) Interaction. Improved moves are generated by exchanging genetic material between samples from different realizations of the same chain, and 2) Optimization. Optimized proposals in terms of likelihood are obtained using a Bayesian extension of the EM algorithm. In addition, a method is devised based on the Akaike information criterion (AIC) for eliminating fictitious clusters that may appear when tracking in a highly cluttered environment. The algorithm's performance is assessed and demonstrated in a tracking scenario consisting of several hundreds targets which form up to six distinct clusters in a highly cluttered environment.
AB - A new filtering algorithm is presented for tracking multiple clusters of coordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithm maintains a discrete approximation of the filtering density of the clusters' state. The filter's tracking efficiency is enhanced by incorporating two stages into the basic Metropolis-Hastings sampling scheme: 1) Interaction. Improved moves are generated by exchanging genetic material between samples from different realizations of the same chain, and 2) Optimization. Optimized proposals in terms of likelihood are obtained using a Bayesian extension of the EM algorithm. In addition, a method is devised based on the Akaike information criterion (AIC) for eliminating fictitious clusters that may appear when tracking in a highly cluttered environment. The algorithm's performance is assessed and demonstrated in a tracking scenario consisting of several hundreds targets which form up to six distinct clusters in a highly cluttered environment.
KW - Evolutionary MCMC
KW - Markov chain Monte Carlo filtering
KW - Multi cluster tracking
KW - Variational Bayesian EM algorithm
UR - http://www.scopus.com/inward/record.url?scp=63649125491&partnerID=8YFLogxK
U2 - 10.1109/DSP.2009.4785932
DO - 10.1109/DSP.2009.4785932
M3 - Conference contribution
AN - SCOPUS:63649125491
SN - 9781424436774
T3 - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
SP - 262
EP - 267
BT - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
T2 - 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
Y2 - 4 January 2009 through 7 January 2009
ER -