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：受现代自主系统中感知模态的启发，这些模态涉及在具有有限相干时间的大规模阵列上进行硬件受限的空间采样，我们开发了一种新型框架，用于基于Hankel结构化感知和任意秩数据矩阵分解的快速超级分辨率多信号波达方向（DoA）估计，该框架同时涵盖$L_2$和$L_1$范数形式。结果表明，所提出的$L_2$范数估计器在高斯白噪声下达到最大似然最优性。$L_1$范数估计器在独立同分布（i.i.d.）各向同性拉普拉斯噪声下达到最大似然最优性，对实践中常见的脉冲干扰和损坏测量具有广泛的鲁棒性。大量仿真表明，所提方法展现出强大的超级分辨率能力，相比近期竞争方法，所需信噪比显著更低，且实现的分辨率概率大幅更高。