Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification

TL;DR

This paper introduces a two-stage distributed algorithm for sensor network localization using TOA data that effectively handles NLOS conditions without prior identification, improving accuracy over existing methods.

Contribution

It proposes a novel two-stage convex relaxation approach with iterative optimization for NLOS-agnostic sensor localization, enhancing accuracy and robustness.

Findings

Lower RMSE compared to existing convex relaxation methods

Refined estimates significantly improve localization accuracy

Achieves near-optimal RMSE without NLOS identification

Abstract

In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know \textit{a priori} which links are in NLOS.