Enhancing Angular Resolution Using Neural Networks in Automotive Radars

Abstract

Poor angular resolution is one of the main disadvantages of automotive radars, and the reason why lidar technology is widely used in the automotive industry. For a fixed frequency, the angular resolution of a conventional Multiple-Input Multiple-Output (MIMO) radar is limited by the number of physical antennas, and therefore improve the resolution involves increasing the size and the cost of the system, critical constraints in the automotive industry. In this work, a novel approach is presented to overcome this limitation, where a Neural Network (NN) is used to enhance the angle resolution of a MIMO radar without increasing the number of physical elements, but extrapolating the antennas signals in a teacher-student fashion. The method was validated using real data of stationary pedestrians captured outdoors, demonstrating an effective increase of three times the antenna array size. To the best knowledge of the authors, this is the first method that includes an evaluation metric in the final stages of the processing pipeline, enforcing the conservation of the target's angular shape, key for subsequent object classification.