Particle filtering for 2-D direction of arrival tracking using an acoustic vector sensor

Acoustic vector sensor (AVS) signal measures acoustic pressure as well as particle velocity, and therefore contains both the azimuth and the elevation information of the source. Existing 2-D DOA estimation methods for AVS assume that the source is static and extensively rely on the localization techniques. In this paper, a particle filtering (PF) approach is developed to track the 2-D DOA by using a single AVS. A constant velocity model is employed to model the source dynamics and the likelihood function is derived based on maximum likelihood estimation of the source amplitude and the noise variance. Since the likelihood function is usually spread and distorted in the heavy noisy environment, it is further exponentially weighted to enhance the weight of particles at high likelihood area. Simulations show that the proposed algorithm significantly outperforms the traditional Capon beamforming method and is able to lock on the DOA of the source in challenging environments.