Cooperative localization using angle of arrival measurements: sequential algorithms and non-line-of-sight suppression

TL;DR

This paper presents a low-complexity sequential algorithm for source localization using AoA measurements in cooperative networks, effectively handling NLOS conditions and outliers through a Bayesian framework and randomized approaches.

Contribution

It introduces a novel linear-complexity sequential localization algorithm and a randomized method that approximates ML performance in NLOS environments.

Findings

The sequential algorithm achieves accurate localization in LOS environments.

The randomized approach approaches ML performance with linear complexity.

Bayesian framework improves robustness in NLOS conditions.

Abstract

We investigate localization of a source based on angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers. The goal is to efficiently compute accurate estimates despite outliers in the AoA measurements due to multipath reflections in non-line-of-sight (NLOS) environments. Maximal likelihood (ML) location estimation in such a setting requires exhaustive testing of estimates from all possible subsets of "good" measurements, which has exponential complexity in the number of measurements. We provide a randomized algorithm that approaches ML performance with linear complexity in the number of measurements. The building block for this algorithm is a low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments. Our Bayesian framework can exploit the ability to resolve multiple paths in wideband systems to provide significant performance gains over narrowband systems in NLOS environments, and easily extends to accommodate additional information such as range measurements and prior information about location.