Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition
This work addresses the need for rapid and robust direction-of-arrival estimation in hardware-constrained autonomous systems, offering improved performance over existing methods.
The paper develops a novel framework for super-resolution multi-signal direction-of-arrival estimation using Hankel-structured sensing and data matrix decomposition, achieving maximum-likelihood optimality in both Gaussian and Laplace noise. Simulations show the methods require significantly lower SNR and achieve substantially higher resolution probability than competing approaches.
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.