Coherent source localization using vector sensor array

This paper addresses the problem of coherent/fully correlated source localization using vector sensor arrays. Initially, the maximum likelihood (ML) estimator for fully correlated source localization using vector sensor arrays is derived to solve the problem. In addition, a novel method for "decorrelating" the incident signals is presented. The method is based on vector sensor smoothing (VSS) and enables the use of eigenstructure-based techniques, which require uncorrelated or partially correlated signals. The method is implemented as a preprocessing stage before applying eigenstructure-based techniques, such as MUSIC. The performance of the proposed VSS preprocessing combined with MUSIC is evaluated and compared to the ML estimator and Cramer-Rao bound (CRB).

2 Introduction

Vector sensors enable estimation of the angle of arrival and polarization of impinging electromagnetic waves with arbitrary polarization. In the last decade, many array processing techniques for source localization and polarization estimation using vector sensors have been developed. Nehorai and Paldi [1] developed the Cramer-Rao bound (CRE) for this problem and the vector cross-product direction-of-arrival (DOA) estimator.