TY - GEN
T1 - Probability hypothesis density filtering with multipath-to-measurement association for urban tracking
AU - Zhou, Meng
AU - Zhang, Jun Jason
AU - Papandreou-Suppappola, Antonia
PY - 2012/10/23
Y1 - 2012/10/23
N2 - We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.
AB - We consider the particle probability hypothesis density filter (PPHDF) for tracking multiple targets in urban terrain. This is a filtering technique based on random finite sets, implemented using the particle filter. Unlike data association methods, the PPHDF can be modified to estimate both the number of targets and their corresponding tracking parameters. We propose a modified PPHDF algorithm that employs multipath-to-measurement association (PPHDF-MMA) to automatically and adaptively estimate the available types of measurements. By using the best matched measurement at each time step, the new algorithm results in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the PPHDF-MMA in improving the tracking performance of multiple targets and targets in clutter.
KW - Urban terrain
KW - multiple target tracking
KW - particle filter
KW - probability hypothesis density filter
UR - http://www.scopus.com/inward/record.url?scp=84867616918&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867616918&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2012.6288614
DO - 10.1109/ICASSP.2012.6288614
M3 - Conference contribution
AN - SCOPUS:84867616918
SN - 9781467300469
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3273
EP - 3276
BT - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Y2 - 25 March 2012 through 30 March 2012
ER -