Orientation Matters: Learning Radiation Patterns of Multi-Rotor UAVs In-Flight to Enhance Communication Availability Modeling

TL;DR

This paper introduces a method to learn and decouple the radiation patterns of heterogeneous UAVs using flight data, improving autonomous communication and control.

Contribution

It proposes a novel calibration approach using polynomial regression and joint trajectories to accurately model UAV antenna radiation patterns.

Findings

Achieved 3.6 dB RMS error in radiation pattern learning.

Demonstrated feasibility of rapid UAV RP recalibration.

Enabled precise autonomous path planning based on learned RPs.

Abstract

The paper presents an approach for learning antenna Radiation Patterns (RPs) of a pair of heterogeneous quadrotor Uncrewed Aerial Vehicles (UAVs) by calibration flight data. RPs are modeled either as a Spherical Harmonics series or as a weighted average over inducing samples. Linear regression of polynomial coefficients simultaneously decouples the two independent UAVs' RPs. A joint calibration trajectory exploits available flight time in an obstacle-free anechoic altitude. Evaluation on a real-world dataset demonstrates the feasibility of learning both radiation patterns, achieving 3.6 dB RMS error, the measurement noise level. The proposed RP learning and decoupling can be exploited in rapid recalibration upon payload changes, thereby enabling precise autonomous path planning and swarm control in real-world applications where setup changes are expected.