Robust Distributed Cooperative RSS-based Localization for Directed Graphs in Mixed LoS/NLoS Environments

TL;DR

This paper introduces a robust distributed localization method for wireless sensor networks that effectively handles mixed LoS/NLoS conditions by adaptively estimating channel parameters and accounting for link asymmetry, ensuring high accuracy and robustness.

Contribution

It presents a novel maximum likelihood-based distributed localization algorithm that models measurements with Gaussian mixtures and adaptively estimates unknown parameters, improving robustness in mixed environments.

Findings

Demonstrates high localization accuracy in simulations.

Shows robustness against LoS/NLoS variability.

Maintains low computational and communication overhead.

Abstract

The accurate and low-cost localization of sensors using a wireless sensor network is critically required in a wide range of today's applications. We propose a novel, robust maximum likelihood-type method for distributed cooperative received signal strength-based localization in wireless sensor networks. To cope with mixed LoS/NLoS conditions, we model the measurements using a two-component Gaussian mixture model. The relevant channel parameters, including the reference path loss, the path loss exponent and the variance of the measurement error, for both LoS and NLoS conditions, are assumed to be unknown deterministic parameters and are adaptively estimated. Unlike existing algorithms, the proposed method naturally takes into account the (possible) asymmetry of links between nodes. The proposed approach has a communication overhead upper-bounded by a quadratic function of the number of nodes and computational complexity scaling linearly with it. The convergence of the proposed method is guaranteed for compatible network graphs and compatibility can be tested a priori by restating the problem as a graph coloring problem. Simulation results, carried out in comparison to a centralized benchmark algorithm, demonstrate the good overall performance and high robustness in mixed LoS/NLoS environments.