Title :
Improved Particle Implementation of the Probability Hypothesis Density Filter in Resampling
Author :
Tang, Xu ; Zhou, Jian ; Huang, Jian ; Wei, Ping
Author_Institution :
Dept. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
A novel particle-PHD filter algorithm is proposed to deal with the multi-target tracking. It takes into account the most recent measurements by the unscented Kalman filter, not in the step of proposal distribution generation as usual, but in resampling step, to enhance the efficiency of the particle sampling. Simulation results show that the proposed algorithm outperforms the algorithms in the literature in performance but with extremely less computational cost.
Keywords :
Kalman filters; particle filtering (numerical methods); probability; sampling methods; target tracking; improved particle implementation; multitarget tracking; novel particle-PHD filter algorithm; particle sampling; probability hypothesis density filter; resampling step; unscented Kalman filter; Approximation algorithms; Atmospheric measurements; Clutter; Filtering algorithms; Information filters; Particle measurements; Clustering; PHD Filter; Particle Filter; UKF; resampling;
Conference_Titel :
Computer and Information Technology (CIT), 2012 IEEE 12th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4673-4873-7
DOI :
10.1109/CIT.2012.36
