Velocity vector estimation in automotive radar networks

Abstract In this work, we develop a method for robust single-cycle measurement velocity vector estimation for automotive radar. Building upon our previous work, we introduce a methodology that leverages spatial diversity for accurate estimation of the velocity vector of targets in the medium to close ranges. We extend our initial conceptual framework, addressing limitations from our first approach and proposing necessary enhancements for real-world applicability. Our improved process excels in target separation, identification, and velocity vector estimation, proving effective across various scenarios and minimizing errors. The system, tested on pedestrians and metal targets, presents a promising avenue for exploring its performance with varying target sizes. Simultaneously, our in-depth study on Doppler-multiplex modulation reveals new relevant constraints, prompting a modulation change for improved response separation. Despite the necessity of increasing module numbers for enhanced performance, our structured approach to target itemization and classification positions our methodology as a valuable framework for future systems, offering a comprehensive solution to diverse challenges in target estimation and classification within the automotive landscape.