TY - GEN
T1 - An Improved Multiple Hypothesis Tracker Integrated with Unknown Clutter Intensity Estimator
AU - Wang, Ziwei
AU - Sun, Jinping
AU - Li, Xudong
AU - Lu, Xiaoke
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/9
Y1 - 2021/4/9
N2 - The multiple hypothesis tracker (MHT) is the optimal multi-target data association algorithm in ideal conditions. However, there is a degraded accuracy of data association when the observation scenario containing unknown clutter density. To addressed this problem, this paper proposed a new multiple hypothesis tracker integrated with unknown clutter intensity estimator (MHT-UCIE). In the MHT-UCIE, the clutter intensity is formulated as the product of the average number of clutter points per scan and the clutter spatial density, which are obtained subsequently through the maximum likelihood estimator and the Gaussian kernel estimator. Finally, the clutter intensity and track hypotheses are calculated. The experiment results verify the superior tracking performance of the MHT- UCIE in an unknown clutter intensity tracking scenario.
AB - The multiple hypothesis tracker (MHT) is the optimal multi-target data association algorithm in ideal conditions. However, there is a degraded accuracy of data association when the observation scenario containing unknown clutter density. To addressed this problem, this paper proposed a new multiple hypothesis tracker integrated with unknown clutter intensity estimator (MHT-UCIE). In the MHT-UCIE, the clutter intensity is formulated as the product of the average number of clutter points per scan and the clutter spatial density, which are obtained subsequently through the maximum likelihood estimator and the Gaussian kernel estimator. Finally, the clutter intensity and track hypotheses are calculated. The experiment results verify the superior tracking performance of the MHT- UCIE in an unknown clutter intensity tracking scenario.
KW - component
KW - maximum likelihood estimator
KW - multiple hypothesis tracker
KW - the Gaussian kernel density estimator.
KW - unknown clutter intensity
UR - https://www.scopus.com/pages/publications/85105455153
U2 - 10.1109/ICSP51882.2021.9408731
DO - 10.1109/ICSP51882.2021.9408731
M3 - 会议稿件
AN - SCOPUS:85105455153
T3 - 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021
SP - 1433
EP - 1437
BT - 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021
Y2 - 9 April 2021 through 11 April 2021
ER -