Research on underwater multi-target tracking algorithm based on BELLHOP ray acoustic model

An Unscented Kalman Filter-Gaussian Mixture Probability Hypothesis Density (UKF-GM-PHD) underwater multi-target tracking algorithm based on the BELLHOP model is proposed for the deep-sea multi-target tracking problem of high-frequency active sonar. Firstly, the BELLHOP ray acoustic model is used for the calculation of the amplitude, phase and time delay information of the intrinsic sound line and target signal and for the construction of target echo signal with Gaussian white noise. Then, the distance, azimuth, and pitch angle of the target relative to the observation station, which are calculated from the constructed echo signal, are used as the measurement information in the target tracking system. Finally, the proposed UKF-GM-PHD multi-target tracking algorithm is applied to multi-target tracking of the nonlinear system of high frequency active sonar. The simulation results show that compared with the traditional Gaussian Mixture Probability Hypothesis Density (GM-PHD) method, the proposed UKF-GM-PHD multi-target tracking algorithm can significantly reduces the target loss rate for deep-sea highfrequency active sonar, and the optimal sub-patter assignment (OSPA) distance is smaller and the tracking effect is better.