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.

翻译：受现代自主系统中涉及硬件受限空间采样（大阵列、有限相干时间）的感知模式启发，我们提出了一种新的快速超分辨率多信号波达方向（DoA）估计框架。该框架基于Hankel结构感知与任意秩的数据矩阵分解，涵盖$L_2$范数与$L_1$范数两种公式。研究表明，所提$L_2$范数估计器在高斯白噪声条件下具有最大似然最优性；而$L_1$范数估计器在独立同分布（i.i.d.）各向同性拉普拉斯噪声中同样具有最大似然最优性，从而对实际中常见的脉冲干扰与测量损坏表现出广泛的鲁棒性。大量仿真实验表明，所提方法具有强大的超分辨率能力，能在显著更低的信噪比（SNR）条件下获得远高于现有竞争方法的分辨率概率。