Hybrid Sensor Fusion Beamforming for UAV mmWave Communication

Abstract

Resilient autonomous inter-Unmanned Aerial Vehicle (UAV) communication is critical for applications like drone swarms. While conventional Global Navigation Satellite System (GNSS)-based beamforming is effective at long ranges, it suffers from significant pointing errors at close range due to latency, low update rates and the inherent GNSS positioning error. To overcome these limitations, this paper proposes a novel hybrid beamforming system that enhances resilience by adaptively switching between two methods. For short-range operations, our system leverages Light Detection and Ranging (LiDAR)–camera sensor fusion for high-accuracy, low-latency UAV tracking, enabling precise millimeter-wave (mmWave) beamforming. For long-range scenarios beyond the camera’s detection limit, it intelligently switches to a GNSS-based method. The switching threshold is determined by considering both the sensor’s effective range and the pointing errors caused by GNSS latency and a UAV velocity. Simulations conducted in a realistic urban model demonstrate that our hybrid approach compensates for the weaknesses of each individual method. It maintains a stable, high-throughput link across a wide range of distances, achieving superior performance and resilience compared to systems relying on a single tracking method. This paves the way for advanced autonomous drone network operations in dynamic environments.