TY - GEN
T1 - On MCMC-based particle methods for bayesian filtering
T2 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009
AU - Septier, François
AU - Pang, Sze Kim
AU - Carmi, Avishy
AU - Godsill, Simon
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. In this context, one of the most successful and popular approximation techniques is Sequential Monte Carlo (SMC) methods, also known as particle filters. Nevertheless, these methods tend to be inefficient when applied to high dimensional problems. In this paper, we present an overview of Markov chain Monte Carlo (MCMC) methods for sequential simulation from posterior distributions, which represent efficient alternatives to SMC methods. Then, we describe an implementation of this MCMCBased particle algorithm to perform the sequential inference for multitarget tracking. Numerical simulations illustrate the ability of this algorithm to detect and track multiple targets in a highly cluttered environment.
AB - Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. In this context, one of the most successful and popular approximation techniques is Sequential Monte Carlo (SMC) methods, also known as particle filters. Nevertheless, these methods tend to be inefficient when applied to high dimensional problems. In this paper, we present an overview of Markov chain Monte Carlo (MCMC) methods for sequential simulation from posterior distributions, which represent efficient alternatives to SMC methods. Then, we describe an implementation of this MCMCBased particle algorithm to perform the sequential inference for multitarget tracking. Numerical simulations illustrate the ability of this algorithm to detect and track multiple targets in a highly cluttered environment.
UR - http://www.scopus.com/inward/record.url?scp=77951112277&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2009.5413256
DO - 10.1109/CAMSAP.2009.5413256
M3 - Conference contribution
AN - SCOPUS:77951112277
SN - 9781424451807
T3 - CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
SP - 360
EP - 363
BT - CAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Y2 - 13 December 2009 through 16 December 2009
ER -