Data Driven Low-Complexity DOA Estimation for Ultra-Short Range Automotive Radar

Abstract

In recent applications of millimeter wave automotive radars, the short range detection and estimation performance becomes an important design metric. Due to the sphere rather than plane form of array incoming signals, direct use of conventional spectrum or direction of arrival (DOA) estimators generally result in large performance degradation. In this paper, a naive look-up table based solution is first introduced. To solve its involved large storage requirement problem, we further transform the DOA estimation problem into the DOA classification problem, and utilize the support vector machine (SVM) framework to propose a data-driven low-complexity DOA estimator. Simulations validate the effectiveness of the propose SVM solution especially for small sample set and high storage limit.