TY - JOUR
T1 - Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
AU - Zhang, Boxuan
AU - Hou, Xianghao
AU - Yang, Yixin
AU - Zhou, Jianbo
AU - Xu, Shengli
N1 - Publisher Copyright:
copyright © 2023 Zhang, Hou, Yang, Zhou and Xu.
PY - 2023
Y1 - 2023
N2 - The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.
AB - The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.
KW - adaptive tracking
KW - cardinalized probability hypothesis density filter
KW - uncertain measurement noise
KW - underwater multi-target direction-of-arrival tracking
KW - variational Bayesian approach
UR - http://www.scopus.com/inward/record.url?scp=85150206521&partnerID=8YFLogxK
U2 - 10.3389/fphy.2023.1142400
DO - 10.3389/fphy.2023.1142400
M3 - 文章
AN - SCOPUS:85150206521
SN - 2296-424X
VL - 11
JO - Frontiers in Physics
JF - Frontiers in Physics
M1 - 1142400
ER -