AUTHOR=Zhang Boxuan , Hou Xianghao , Yang Yixin , Zhou Jianbo , Xu Shengli 

TITLE=Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

JOURNAL=Frontiers in Physics

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1142400

DOI=10.3389/fphy.2023.1142400

ISSN=2296-424X

ABSTRACT=Direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering 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. Firstly, the kinematic model of multiple underwater targets and the bearing angle measurement model with missing detection and false alarm are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density filter, whose performance 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 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.