A Novel Gridless Non-Uniform Linear Array Direction of Arrival Estimation Approach Based on the Improved Alternating Descent Conditional Gradient Algorithm for Automotive Radar System

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared t...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 17; číslo 2; s. 303
Hlavní autoři: Shao, Mingxiao, Fan, Yizhe, Zhang, Yan, Zhang, Zhe, Zhao, Jie, Zhang, Bingchen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2025
Témata:
Accuracy
Algorithms
Arrays
Automotive radar
compressive sensing
Direction of arrival
DOA estimation
Effectiveness
Linear arrays
Millimeter waves
millimeter-wave radar
non-uniform linear array
off-grid effect
Radar
Radar arrays
Radar equipment
Radar systems
Signal classification
Signal processing
ISSN:2072-4292, 2072-4292
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Shrnutí:In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs17020303