Direction-of-Arrival Estimation With Time-Varying Arrays via Bayesian Multitask Learning

This paper proposes a Bayesian method to address the farfield narrowband direction-of-arrival (DOA) estimation problem with time-varying arrays, whose elements relatively move in an arbitrary but known way. The measurements associated with different array geometries are formulated with distinct and spatially overcomplete observation systems, and a joint Bayesian model is established to combine those measurements and yield unified DOA estimates. The joint reconstruction process of the multiple measurements falls into the multitask learning category; thus, the proposed method is named DOA estimation via multitask learning (DEML). Theoretical results focusing on the uniqueness of the solution and the global convergence of the Bayesian learning process are also given, which indicate the maximal separable signal number and the global convergence of the proposed method in the considered array processing scenarios. Numerical examples are also provided to demonstrate the DOA estimation performance of the proposed method and support the theoretical results.