A Facial Reduction Approach for the Single Source Localization Problem

TL;DR

This paper introduces a facial reduction method to efficiently solve the nonconvex single source localization problem by reformulating it as a rank constrained Euclidean distance matrix completion problem, ensuring fast convergence and solution quality.

Contribution

It presents a novel facial reduction approach that reduces the problem to a face of the EDM cone and guarantees quadratic convergence for the resulting optimization.

Findings

The proposed method converges quickly in numerical experiments.

It achieves solution quality comparable to existing methods.

The approach effectively handles the nonconvex rank constraint.

Abstract

The single source localization problem (SSLP) appears in several fields such as signal processing and global positioning systems. The optimization problem of SSLP is nonconvex and difficult to find its globally optima solution. It can be reformulated as a rank constrained Euclidean distance matrix (EDM) completion problem with a number of equality constraints. In this paper, we propose a facial reduction approach to solve such an EDM completion problem. For the constraints of fixed distances between sensors, we reduce them to a face of the EDM cone and derive the closed formulation of the face. We prove constraint nondegeneracy for each feasible point of the resulting EDM optimization problem without a rank constraint, which guarantees the quadratic convergence of semismooth Newton's method. To tackle the nonconvex rank constraint, we apply the majorized penalty approach developed by Zhou et al. (IEEE Trans Signal Process 66(3):4331-4346, 2018). Numerical results verify the fast speed of the proposed approach while giving comparable quality of solutions as other methods.