Optimal Measurement Policy for Predicting UAV Network Topology

TL;DR

This paper proposes an optimal tracking policy for UAV networks to predict topology changes efficiently, utilizing particle swarm optimization and Kalman filtering to minimize network estimation errors amid dynamic conditions.

Contribution

It introduces a novel algorithm combining particle swarm optimization and Kalman filtering for UAV network topology prediction under resource constraints.

Findings

Algorithm effectively reduces network estimation error.

Optimizes UAV tracking policies in dynamic environments.

Enhances communication reliability in UAV networks.

Abstract

In recent years, there has been a growing interest in using networks of Unmanned Aerial Vehicles (UAV) that collectively perform complex tasks for diverse applications. An important challenge in realizing UAV networks is the need for a communication platform that accommodates rapid network topology changes. For instance, a timely prediction of network topology changes can reduce communication link loss rate by setting up links with prolonged connectivity. In this work, we develop an optimal tracking policy for each UAV to perceive its surrounding network configuration in order to facilitate more efficient communication protocols. More specifically, we develop an algorithm based on particle swarm optimization and Kalman filtering with intermittent observations to find a set of optimal tracking policies for each UAV under time-varying channel qualities and constrained tracking resources such that the overall network estimation error is minimized.