Explicit Sensor Network Localization using Semidefinite Representations and Facial Reductions

TL;DR

This paper introduces a novel method for sensor network localization that explicitly solves the rank-restricted SDP problem without using SDP solvers, efficiently handling large instances by exploiting facial reduction techniques.

Contribution

It develops a face-based approach that explicitly solves the rank-constrained SDP for sensor localization, avoiding expensive SDP solvers and improving efficiency for large problems.

Findings

Successfully solves large, NP-hard instances with high accuracy.

Avoids the use of SDP solvers by exploiting facial reduction.

Efficiently finds minimal faces of the SDP cone for localization.

Abstract

The sensor network localization, SNL, problem in embedding dimension r, consists of locating the positions of wireless sensors, given only the distances between sensors that are within radio range and the positions of a subset of the sensors (called anchors). Current solution techniques relax this problem to a weighted, nearest, (positive) semidefinite programming, SDP, completion problem, by using the linear mapping between Euclidean distance matrices, EDM, and semidefinite matrices. The resulting SDP is solved using primal-dual interior point solvers, yielding an expensive and inexact solution. This relaxation is highly degenerate in the sense that the feasible set is restricted to a low dimensional face of the SDP cone, implying that the Slater constraint qualification fails. Cliques in the graph of the SNL problem give rise to this degeneracy in the SDP relaxation. In this paper, we take advantage of the absence of the Slater constraint qualification and derive a technique for the SNL problem, with exact data, that explicitly solves the corresponding rank restricted SDP problem. No SDP solvers are used. For randomly generated instances, we are able to efficiently solve many huge instances of this NP-hard problem to high accuracy, by finding a representation of the minimal face of the SDP cone that contains the SDP matrix representation of the EDM. The main work of our algorithm consists in repeatedly finding the intersection of subspaces that represent the faces of the SDP cone that correspond to cliques of the SNL problem.