基于稀疏贝叶斯学习的多波束频率扫描lda估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-06 DOI:10.1109/LGRS.2025.3577369

Qingqing Zhu;Biyun Ma;Yide Wang;Jiaojiao Liu;Yuehui Cui

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引用次数: 0

摘要

多波束频率波束扫描漏波天线（FBS-LWAs）实现了带宽减少的紧凑系统，但引入了寄生干扰，损害了到达方向（DOA）估计。为了解决这一问题，本文提出了一种针对多波束fbs - lwa的新的DOA估计方法。理论分析表明，真实的doa与辐射方向图中的多个峰一致。在此基础上，在频率空间域通过网格细化构造裁剪超完备集。然后采用奇异值分解（SVD-SBL）增强的稀疏贝叶斯学习（SBL）算法抑制寄生峰，准确恢复真实doa。仿真结果验证了该方法的有效性和实用性。

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DOA Estimation of Multibeam Frequency Beam Scanning LWAs Based on Sparse Bayesian Learning

Multibeam frequency beam scanning leaky wave antennas (FBS-LWAs) enable a compact system with reduced bandwidth but introduce parasitic interference that impairs the direction-of-arrival (DOA) estimation. To solve this issue, this letter proposes a novel DOA estimation method tailored for multibeam FBS-LWAs. Theoretical analysis reveals that the true DOAs align with multiple peaks in the radiation pattern. Based on this insight, a cropped hypercomplete set is constructed via grid refinement in the frequency-space domain. The sparse Bayesian learning (SBL) algorithm, enhanced by singular value decomposition (SVD-SBL), is then employed to suppress parasitic peaks and accurately recover the true DOAs. The simulation results demonstrate the proposed method’s effectiveness androbustness.

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IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society

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