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

arXiv: https://arxiv.org/abs/2604.26793 · HTML: https://mlsanity.com/paper/2604.26793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 68 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Robust Spectral Compressed Sensing via Structured Matrix Completion](https://mlsanity.com/paper/1304.8126/) (2013)
- [Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification](https://mlsanity.com/paper/2110.14789/) (2021)
- [Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation](https://mlsanity.com/paper/1902.03569/) (2019)
- [Towards Fine-Grained Indoor Localization based on Massive MIMO-OFDM System: Experiment and Analysis](https://mlsanity.com/paper/2103.14863/) (2021)
- [Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation](https://mlsanity.com/paper/2309.07411/) (2023)
- [Coverage Analysis of UAVs in Millimeter Wave Networks: A Stochastic Geometry Approach](https://mlsanity.com/paper/2003.01391/) (2020)
- [Path Planning Under Uncertainty to Localize mmWave Sources](https://mlsanity.com/paper/2303.03739/) (2023)

## Abstract (arXiv)

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 L2 and L1-norm formulation. The resulting L2-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The L1-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.