Unsupervised Online Horizontal Misalignment Detection Algorithm for Automotive Radar

Abstract

This paper proposes a stationary target based online unsupervised calibration algorithm that can be applied on both 4D and 3D automotive radars for its horizontal alignment and misalignment detection. The calibration process requires no special EOL (End of Line) setup or stationary structures of reference. The method is based on the accurate determination of the own vehicle velocity and by using stationary targets. The approach provides both a long-term stable azimuth mounting compensation value as well as a separate, more dynamic angle value that converges faster than the long-term value in case of small accidents. The proposed method considers the systematic errors resulted from the vehicle integration and bumper tolerances and delivers an accurate horizontal alignment correction by using filtering outlier rejection techniques. The performance is evaluated used real world data from drive tests executed with a 77 GHz series automotive radar, showing promising results.