Detecting outliers in the estimator bank-based direction finding techniques using the likelihood ratio quality assessment

Abstract

Estimator bank-based direction finding techniques make use of a number of parallel randomly weighted MUSIC direction-of-arrival (DOA) estimates and obtain the final estimate by keeping only the "successful" candidates while sorting out the outlying estimates. In this paper, we develop a powerful approach to detect the outliers in the estimator bank-based direction finders using the likelihood ratio quality assessment. Computer simulations show substantial improvements of the proposed approach as compared to the earlier techniques used to sort out the outliers in the estimator bank-based direction finding methods.