Distributed localization of a RF target in NLOS environments

TL;DR

This paper introduces a distributed EM algorithm for RF target localization in NLOS environments using sensor networks, enabling accurate, robust, and spectrum-efficient localization without centralized data collection.

Contribution

It presents a novel distributed EM approach for non-cooperative RF localization in NLOS scenarios, reducing spectrum use and increasing robustness compared to centralized methods.

Findings

Achieves localization accuracy close to centralized EM algorithms.

Converges reliably in simulated and real-world NLOS scenarios.

Enhances robustness against sensor node failures.

Abstract

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.