PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter

Sensors

Xiaohua Li¹  Yaan Li²  Jing Yu²  Xiao Chen²  Miao Dai² 

[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China;

关键词: probabilistic multi-hypothesis tracker (PMHT);    multi-target multi-sensor sonar tracking;    extended Kalman filter (EKF);    unscented Kalman filter (UKF);    data association;   

DOI  :  10.3390/s151128177

来源: mdpi

【 摘 要 】

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.