Performance Characterization of Sparse Array Angle Finding Algorithms for Automotive Radars

This is the authors' abstract. We don't add key points for this paper.

This study introduces two novel coherent-signal single-snapshot sparse array high resolution angle finding (HRAF) algorithms: the Binary Search Matching Pursuit (BSMP) and the Bayesian Linear Regression with Cauchy Prior (BLRC). To provide a comprehensive and quantitative evaluation of these HRAF algorithms, we develop four key performance indicators (KPls): the probability of target separation, the 95th percentile direction-of-arrival (DoA) estimation error, the 95th percentile number of spurious targets, and the 95th percentile power of spurious targets. The analysis conducted in this study not only highlights the strengths and potential applications of the BSMP and BLRC algorithms but also sets a new benchmark for evaluating various HRAF algorithms in the context of advanced automotive radar systems.