Sparse DOA Estimation With Polarimetric Arrays

This paper addresses the Direction-of-Arrival (DOA) estimation problem using a narrowband polarimetric array sensing system. The considered receiving equipment is composed of two sub-arrays of sensors with orthogonal polarizations. By suitably modeling the received signal via a sparse representation (accounting for the multiple snapshots and the polarimetric array manifold structure), two iterative algorithms, namely Polarimetric Sparse Learning via Iterative Minimization (POL-SLIM) and Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE), are devised to accomplish the estimation task. The proposed algorithms provide accurate DOA estimates while enjoying nice (rigorously proven) convergence properties. Numerical analysis shows the effectiveness of POL-SLIM and POL-SPICE to successfully locate signal sources in both passive sensing applications (with large numbers of collected snapshots) and radar spatial processing, also in comparison with single-polarization counterparts as well as theoretical benchmarks.