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

2026-04-29 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Signal Processing · Depth: Expert, quick

Summary

A new framework for rapid super-resolution multi-signal direction-of-arrival (DoA) estimation has been developed, addressing hardware-constrained spatial sampling in autonomous systems. This framework utilizes Hankel-structured sensing and data matrix decomposition, supporting arbitrary rank under both L2 and L1-norm formulations. The L2-norm estimator achieves maximum-likelihood optimality in white Gaussian noise, while the L1-norm estimator is maximum-likelihood optimal in i.i.d. isotropic Laplace noise, providing robustness against impulsive interference and corrupted measurements. Extensive simulations confirm that these proposed methods offer powerful super-resolution capabilities, requiring significantly lower SNR and achieving substantially higher resolution probability compared to recent competing approaches.

Key takeaway

For signal processing engineers designing autonomous systems, this new DoA estimation framework provides a robust solution for super-resolution in challenging environments. You should consider integrating these L1/L2-norm optimal estimators to improve accuracy and resilience against noise and interference, especially when dealing with hardware constraints and limited coherence time.

Key insights

A new DoA estimation framework offers super-resolution with robustness to noise and interference.

Principles

• L2-norm is optimal for Gaussian noise.
• L1-norm is optimal for Laplace noise.

Method

The framework uses Hankel-structured sensing and data matrix decomposition for multi-signal DoA estimation.

In practice

• Enhances DoA in autonomous systems.
• Improves resolution in low SNR.

Topics

• Direction-of-Arrival Estimation
• Super-resolution Sensing
• Hankel-structured Sensing
• Data Matrix Decomposition
• L2-norm Estimation

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.