A Covariance-Based Superpositional CPHD Filter for Multisource DOA Tracking

Direction of arrival (DOA) estimation of multiple sources using an array of sensors is a well-known problem in signal processing. Most DOA estimation methods use second-order measurements in the form of covariance matrices obtained from consecutive snapshots of the array elements’ raw data. In this paper, we pose the covariance-based DOA estimation problem in the random finite set framework of multitarget tracking using a superpositional model. The superpositional model allows for the measurements to be directly incorporated into a track-before-detect approximate cardinalized probability hypothesis density filter with a likelihood distributed as a complex Wishart random matrix that can perform DOA tracking with unknown time-varying number of sources. Complex Wishart and inverse-Wishart conjugacy is employed to derive the filter's update equations. The proposed filter is implemented using an auxiliary particle filter and simulation results showcase its improved performance in challenging scenarios of low (negative) signal-to-noise ratio and small number of snapshots.