Localization from Incomplete Noisy Distance Measurements

TL;DR

This paper introduces a semidefinite programming-based algorithm for localizing points in Euclidean space from incomplete and noisy distance data, providing theoretical performance guarantees in random geometric graph models.

Contribution

It offers a novel reconstruction method with precise performance bounds for both noiseless and noisy scenarios in the context of incomplete distance measurements.

Findings

Exact reconstruction radius in noiseless case

Upper and lower bounds on reconstruction error with noise

Performance characterization in random geometric graphs

Abstract

We consider the problem of positioning a cloud of points in the Euclidean space $\mathbb{R}^d$, using noisy measurements of a subset of pairwise distances. This task has applications in various areas, such as sensor network localization and reconstruction of protein conformations from NMR measurements. Also, it is closely related to dimensionality reduction problems and manifold learning, where the goal is to learn the underlying global geometry of a data set using local (or partial) metric information. Here we propose a reconstruction algorithm based on semidefinite programming. For a random geometric graph model and uniformly bounded noise, we provide a precise characterization of the algorithm's performance: In the noiseless case, we find a radius $r_0$ beyond which the algorithm reconstructs the exact positions (up to rigid transformations). In the presence of noise, we obtain upper and lower bounds on the reconstruction error that match up to a factor that depends only on the dimension $d$, and the average degree of the nodes in the graph.