On a registration-based approach to sensor network localization

TL;DR

This paper introduces a registration-based method for sensor network localization that efficiently partitions the network into overlapping cliques, registers them globally, and demonstrates improved performance over existing methods.

Contribution

It presents a novel approach combining clique partitioning, rigidity testing, and semidefinite relaxation for scalable sensor network localization.

Findings

Method outperforms state-of-the-art in run-time

Achieves higher accuracy in localization

Scales well to large networks

Abstract

We consider a registration-based approach for localizing sensor networks from range measurements. This is based on the assumption that one can find overlapping cliques spanning the network. That is, for each sensor, one can identify geometric neighbors for which all inter-sensor ranges are known. Such cliques can be efficiently localized using multidimensional scaling. However, since each clique is localized in some local coordinate system, we are required to register them in a global coordinate system. In other words, our approach is based on transforming the localization problem into a problem of registration. In this context, the main contributions are as follows. First, we describe an efficient method for partitioning the network into overlapping cliques. Second, we study the problem of registering the localized cliques, and formulate a necessary rigidity condition for uniquely recovering the global sensor coordinates. In particular, we present a method for efficiently testing rigidity, and a proposal for augmenting the partitioned network to enforce rigidity. A recently proposed semidefinite relaxation of global registration is used for registering the cliques. We present simulation results on random and structured sensor networks to demonstrate that the proposed method compares favourably with state-of-the-art methods in terms of run-time, accuracy, and scalability.