Bayesian direction-of-arrival estimation for moving sound source in high-interference environments

This paper investigates direction-of-arrival (DOA) estimation for moving acoustic sources in high-interference environments. Randomly distributed strong interference sources blur the angular discrimination between the target and the interference sources, significantly degrading the localization accuracy of conventional DOA estimation methods. A Bayesian DOA estimation method is proposed for moving sound sources in high-interference environments, which systematically integrates the prior knowledge of target location from a tracker into the multiple signal classification (MUSIC) spectral estimation framework. The DOA estimation problem is transformed into a constrained array spatial spectrum optimization problem, and a Bayesian MUSIC estimator based on a branch-and-bound algorithm is presented to efficiently obtain high-precision DOA measurements, which are subsequently integrated into an extended Kalman filter (EKF) tracker. The proposed method exhibits higher accuracy and robustness as well as lower complexity than the existing benchmark methods in high-interference environments.