用汉克尔结构传感实现快速高分辨多信号波达方向估计
Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition
- 基于汉克尔结构传感与任意秩数据矩阵分解，实现快速超分辨波达方向估计
- 在低信噪比下仍保持高分辨概率，优于现有主流方法
- 对脉冲干扰和异常测量具有强鲁棒性，适合实际硬件受限场景
针对现代自主系统中大型阵列受硬件约束的空间采样及有限相干时间问题，本文提出一种新型快速超分辨多信号波达方向（DoA）估计框架，基于汉克尔结构传感与任意秩数据矩阵分解，适用于 $L_2$ 和 $L_1$-范数建模。$L_2$-范数估计器在白高斯噪声下为最大似然最优；$L_1$-范数估计器在独立同分布各向同性拉普拉斯噪声下为最大似然最优，对脉冲干扰和异常测量具有广泛鲁棒性。大量仿真表明，该方法在显著更低的信噪比下即可实现远超现有竞争方法的分辨概率。
原文摘要 · Abstract (English)
Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence time, we develop a novel framework for rapid super-resolution multi-signal direction-of-arrival (DoA) estimation based on Hankel-structured sensing and data matrix decomposition of arbitrary rank, under both the $L_2$ and $L_1$-norm formulation. The resulting $L_2$-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The $L_1$-norm estimator is shown to be maximum-likelihood optimal in independent, identically distributed (i.i.d.) isotropic Laplace noise, offering broad robustness to impulsive interference and corrupted measurements commonly encountered in practice. Extensive simulations demonstrate that the proposed methods exhibit powerful super-resolution capabilities, requiring significantly lower SNR and achieving substantially higher resolution probability than recent competing approaches.
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