A Fast Variational Bayesian Adaptive Extended Kalman Filter for Robust Underwater Direction-of-Arrival Tracking

Abstract

The underwater direction-of-arrival (DOA) tracking using hydrophone array is an important research subject in passive sonar signal processing. To deal with the unknown measurement noise results from underwater environmental noise, the existing variational Bayesian adaptive extended Kalman filter (VB-AEKF) technique for robust underwater DOA tracking jointly estimates the measurement noise covariance matrix and the bearing angle of a target. The iteration process and the nonlinear measurement model result in significant computational complexity of the VB-AEKF. By deriving computational process which is equivalent to VB-AEKF but more computationally efficient, a fast variational Bayesian adaptive extended Kalman filter (FVB-AEKF) for robust DOA tracking is proposed. The proposed method is tested via Monte Carlo simulations of an underwater DOA tracking scenario and an experiment in the South China Sea in July 2021. The results of the simulations and the experiment verified the accuracy and the computational efficiency of the FVB-AEKF.