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

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.

Figures (8)

• Figure A1: Illustration of our work. uav A approaches uav B four times; each time, however, attaining a different relative antenna orientation. Both antennas were always horizontal, parallel to the ground. However, antenna A bore a different heading on each approach. As apparent from the recorded rssi, choosing an appropriate orientation leads to consistent signal strength gain of around 10dB.
• Figure B1: Radiation pattern geometry. Two uav see each other at independent azimuths $\alpha_b^a$ and $\alpha_a^b$. Although inclinations $\beta_b^a$ and $\beta_a^b$ are opposite due to uav non-holonomy, we still consider both. Pairs of angles $\alpha,\beta$ comprise the respective aoi. Each uav has an antenna affixed somewhere near its local reference frame origin; together with the uav structure, each uav-with-antenna system becomes a complex antenna. The patterns are illustrated as red and blue blobs.
• Figure D1: Learning trajectory: coordinates for both uav plotted in a common local coordinate frame. X coordinate was constant 0m. Effectively, uav synchronously tracked a vertical circle with a constant diameter of 10m with a fixed center 20m above the ground. After each loop, one of the uav slightly changed its heading to scan all relative poses.
• Figure D2: Illustration of the methodSphericalHarmonics basis functions, $l\in\left\{0,1,2,3\right\}$. Horizontal (x) axes denote azimuth, vertical (y) axes denote inclination.
• Figure E1: Used uav platforms and the experimental venue.