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

arXiv cs.LG / 4/30/2026

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Key Points

The paper introduces a new framework for fast super-resolution direction-of-arrival (DoA) estimation for multi-signal settings using Hankel-structured sensing under hardware-constrained spatial sampling.
It formulates the problem with both L2- and L1-norm objectives, showing the L2 estimator is maximum-likelihood optimal in white Gaussian noise.
It also proves the L1 estimator is maximum-likelihood optimal under i.i.d. isotropic Laplace noise, aiming for robustness against impulsive interference and corrupted measurements.
Simulation results indicate the approach outperforms recent methods, achieving higher resolution probability at significantly lower SNR.
The work targets practical autonomous-system sensing constraints with arbitrary-rank data-matrix decomposition to enable rapid estimation.

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.