Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group.
We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding, and aligning uniquely realizable subsets of neighboring sensors called patches. In the noise-free case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation, and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity, and running time. While our approach is applicable to higher dimensions, in the current article, we focus on the two-dimensional case.
Program in Applied and Computational Mathematics (PACM), Princeton University, Fine Hall, Washington Road, Princeton VJ 08544-1000; mcucurin@math.princeton.edu.
Mihai
M
Cucuringu
Yaron
Y
Lipman
Amit
A
Singer
Sensor networks; distance geometry; eigenvectors; rigidity theory; spectral graph theory; synchronization