UAV-aided Wireless Node Localization Using Hybrid Radio Channel Models

TL;DR

This paper introduces a hybrid radio channel model combining traditional path loss and neural network-based antenna gain approximation to improve UAV-assisted ground user localization using RSS measurements.

Contribution

It proposes a novel hybrid channel model and an estimation method that jointly estimate channel parameters and UAV-user location, enhancing localization accuracy.

Findings

The hybrid model improves localization accuracy over traditional models.

The neural network effectively approximates UAV antenna gain patterns.

Simulation and real-world tests validate the approach.

Abstract

This paper considers the problem of ground user localization based on received signal strength (RSS) measurements obtained by an unmanned aerial vehicle (UAV). We treat UAV-user link channel model parameters and antenna radiation pattern of the UAV as unknowns that need to be estimated. A hybrid channel model is proposed that consists of a traditional path loss model combined with a neural network approximating the UAV antenna gain function. With this model and a set of offline RSS measurements, the unknown parameters are estimated. We then employ the particle swarm optimization (PSO) technique which utilizes the learned hybrid channel model along with a 3D map of the environment to accurately localize the ground users. The performance of the developed algorithm is evaluated through simulations and also real-world experiments.