Dynamic Dictionary Design for Localization in Automotive Radar Systems

Abstract

This paper proposes a dynamic orthogonal matching pursuit (OMP)-based localization for automotive radar systems. At each time instant, three dictionaries are designed based on prior information on targets’ approximate locations. We use mutual coherence as the criterion for designing each dictionary. The mutual coherence minimization problem over each dictionary is developed as a selection problem in the binary domain. Afterward, the OMP algorithm is used to perform the direction of arrival (DoA) estimation over each dictionary, and the estimated DoAs are fused together through averaging to obtain the final DoA estimation. We show that the proposed method significantly outperforms the uniform grid dictionary and improves localization accuracy.