Single-Snapshot Direction of Arrival Estimation for Vehicle-Mounted Millimeter-Wave Radar via Fast Deterministic Maximum Likelihood Algorithm

• Published in: World Electric Vehicle Journal
• Main Authors: Hong Liu, Han Xie, Zhen Wang, Xianling Wang, Donghang Chai
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: DoA estimation; FDML; millimeter-wave radar; super-resolution; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; Transportation engineering; TA1001-1280; DML
• Online Access: https://doi.org/10.3390/wevj15070321; https://doaj.org/article/c02be6b575c44735b8214344510d5d07

As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement strategy based on the Fast Deterministic Maximum Likelihood (FDML) algorithm is proposed in this paper. This strategy sequentially uses Digital Beamforming (DBF) and Deterministic Maximum Likelihood (DML) in the Field of View (FoV) to perform a rough search and precise search, respectively. In a simulation with a signal-to-noise ratio of 20 dB, FDML can accurately determine the target angle in just 16.8 ms, with a positioning error of less than 0.7010. DBF, the Iterative Adaptive Approach (IAA), DML, Fast Iterative Adaptive Approach (FIAA), and FDML are subjected to simulation with two targets, and their performance is compared in this paper. The results demonstrate that under the same angular resolution, FDML reduces computation time by <semantics> 99.30 % </semantics> and angle measurement error by <semantics> 87.17 % </semantics> compared with the angular measurement results of two targets. The FDML algorithm significantly improves computational efficiency while ensuring measurement performance. It provides more reliable technical support for autonomous vehicles and lays a solid foundation for the advancement of autonomous driving technology.