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

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 an...

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Bibliographic Details
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
Description
Summary: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.

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