From Partial Calibration to Full Potential: A Two-Stage Sparse DOA Estimation for Incoherently-Distributed Sources with Gain-Phase Uncertainty

TL;DR

This paper introduces a two-stage sparse DOA estimation method for incoherently distributed sources that effectively handles gain-phase uncertainties and improves accuracy in multipath wireless scenarios.

Contribution

It proposes a novel two-stage framework that transitions from partial calibration to full potential, enhancing DOA estimation accuracy under gain-phase uncertainties.

Findings

Improved DOA estimation accuracy over existing methods

Robustness against noise and angular spread effects

Effective compensation for gain-phase uncertainties

Abstract

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is essential in multipath wireless communication scenarios, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method attained improved estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical multipath environments.