Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks

Abstract

A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the observations from neighboring nodes. Then, the sifted observations are fused to update the state vectors in the CKF. Since nodes in a sensor network have different reliabilities, the final tracking result integrates the estimations from the local nodes, which are weighted with the parameters depending on the mean square error of the estimation and the energy of the received signal. The experimental results illustrated that the proposed PDA-DCKF method is superior to the other DCKF methods in tracking sound sources even under severe noise and reverberant conditions.