Multi-modality likelihood based particle filtering for 2-D direction of arrival tracking using a single acoustic vector sensor

Abstract

The general problem addressed in this paper is tracking the 2-D direction of arrival (DOA) of an acoustic source signal by using a single acoustic vector sensor (AVS). A Bayesian framework and its particle filtering implementation are introduced to adapt to the underwater ambient noise environment, in which both the interference and background noise exist. Several innovations are explored here: 1) a particle filtering based acoustic source tracking algorithm for AVS is developed; and 2) by using a multi-modality likelihood model to model the source detection and false alarm separately, the algorithm is able to alleviate the effect due to noise and interference. Particularly, by employing additional acoustic information, the proposed approach is able to track the 2-D DOA by using a single AVS. The performance of proposed approach is fully investigated under different simulated ambient noisy environments. Experiment results show that the proposed algorithm outperforms the traditional Capon beamforming approach and is able to lock on the 2-D DOA of the source even in a very challenging environment.