Acoustic Beamforming for Object-relative Distance Estimation and Control in Unmanned Air Vehicles using Propulsion System Noise

TL;DR

This paper introduces an acoustic beamforming algorithm that leverages propulsion noise for accurate object-relative distance estimation and control in UAVs, enhancing robustness in noisy environments with minimal additional hardware.

Contribution

The paper presents a novel use of propulsion system noise with a small microphone array for reliable distance estimation and control in UAVs, outperforming baseline methods.

Findings

Achieves over 2x greater range than baseline channel-based methods.

Demonstrates robustness in various practical noise and vehicle conditions.

Effective in closed-loop distance feedback control in real UAV tests.

Abstract

Unmanned air vehicles often produce significant noise from their propulsion systems. Using this broadband signal as "acoustic illumination" for an auxiliary sensing system could make vehicles more robust at a minimal cost. We present an acoustic beamforming-based algorithm that estimates object-relative distance with a small two-microphone array using the generated propulsion system noise of a vehicle. We demonstrate this approach in several closed-loop distance feedback control tests with a mounted quad-rotor vehicle in a noisy environment and show accurate object-relative distance estimates more than 2x further than the baseline channel-based approach. We conclude that this approach is robust to several practical vehicle and noise situations and shows promise for use in more complex operating environments.