TY - GEN
T1 - Multi-Target Tracking with the Progressive Gaussian Probability Hypothesis Density Filter
AU - Wang, Junjie
AU - Zhao, Lingling
AU - Su, Xiaohong
AU - Shi, Chunmei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - Particle flow filter implementations of random finite set filters have been proposed to tackle the issue of jointly estimating the number of targets and states. However, errors resulting from linearization are unavoidable. This paper presents a progressive Gaussian implementation of the probability hypothesis density filter, called the PG-PHD filter. The PG-PHD filter employed the progressive Gaussian filter to predict and update instead of the particle flow filter. The proposed algorithm addresses the drawback of Gaussian particle flow filter by using the progressive Gaussian method to migrate particles to the dense regions of the posterior while no need to linear the measurement function. The simulation results show that the performance of proposed PG-PHD improved significantly compared with the particle flow PHD filter.
AB - Particle flow filter implementations of random finite set filters have been proposed to tackle the issue of jointly estimating the number of targets and states. However, errors resulting from linearization are unavoidable. This paper presents a progressive Gaussian implementation of the probability hypothesis density filter, called the PG-PHD filter. The PG-PHD filter employed the progressive Gaussian filter to predict and update instead of the particle flow filter. The proposed algorithm addresses the drawback of Gaussian particle flow filter by using the progressive Gaussian method to migrate particles to the dense regions of the posterior while no need to linear the measurement function. The simulation results show that the performance of proposed PG-PHD improved significantly compared with the particle flow PHD filter.
KW - Multi-Target Tracking
KW - Probability Hypothesis Density
KW - Progressive Gaussian Filter
KW - Random Finite Set
UR - https://www.scopus.com/pages/publications/85060287642
U2 - 10.1109/ICCAIS.2018.8570434
DO - 10.1109/ICCAIS.2018.8570434
M3 - 会议稿件
AN - SCOPUS:85060287642
T3 - ICCAIS 2018 - 7th International Conference on Control, Automation and Information Sciences
SP - 78
EP - 83
BT - ICCAIS 2018 - 7th International Conference on Control, Automation and Information Sciences
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Control, Automation and Information Sciences, ICCAIS 2018
Y2 - 24 October 2018 through 27 October 2018
ER -