Communication-efficient Coordinated RSS-based Distributed Passive Localization via Drone Cluster

TL;DR

This paper introduces a communication-efficient distributed localization method for passive emitter tracking using UAV clusters, leveraging RSS measurements and Fisher information to improve accuracy and reduce communication overhead.

Contribution

It proposes a novel distributed majorization-minimization localization method and a low-communication estimation scheme using Fisher information, enhancing accuracy and efficiency in UAV-based passive localization.

Findings

DMM outperforms DGN in RMSE under low communication constraints.

DEF achieves better RMSE than MM but with higher computational complexity.

Simulation results validate the effectiveness of the proposed methods.

Abstract

Recently, passive unmanned aerial vehicle (UAV) localization has become popular due to mobility and convenience. In this paper, we consider a scenario of using distributed drone cluster to estimate the position of a passive emitter via received signal strength (RSS). First, a distributed majorizeminimization (DMM) RSS-based localization method is proposed. To accelerate its convergence, a tight upper bound of the objective function from the primary one is derived. Furthermore, to reduce communication overhead, a distributed estimation scheme using the Fisher information matrix (DEF) is presented, with only requiring one-round communication between edge UAVs and center UAV. Additionally, a local search solution is used as the initial value of DEF. Simulation results show that the proposed DMM performs better than the existing distributed Gauss-Newton method (DGN) in terms of root of mean square error (RMSE) under a limited low communication overhead constraint. Moreover, the proposed DEF performs much better than MM in terms of RMSE, but has a higher computational complexity than the latter.