Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition

2026-04-29

AI Summary

This work addresses the challenge of super-resolution direction-of-arrival (DoA) estimation for multiple signals in large-scale arrays under hardware constraints and short coherence times. The authors propose a novel framework that integrates Hankel structure-aware modeling with arbitrary-rank data matrix decomposition. Two DoA estimators are formulated—one based on the L₂ norm, which achieves maximum likelihood optimality under white Gaussian noise, and another based on the L₁ norm, which exhibits strong robustness in Laplacian noise. As the first approach to combine Hankel structure awareness with arbitrary-rank decomposition for DoA estimation, the proposed method significantly reduces the required signal-to-noise ratio and substantially improves resolution probability, outperforming existing techniques across diverse noise environments.

Abstract

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.

Problem

• super-resolution
• direction-of-arrival estimation
• Hankel-structured sensing
• multi-signal
• spatial sampling

Innovation

• Hankel-structured sensing
• super-resolution DoA estimation
• matrix decomposition
• L1-norm robustness
• maximum-likelihood optimality