An Approach to Direction-of-Arrival Estimation for Airborne Microphone Arrays

Direction-of-arrival (DOA) estimation and localization of acoustic sources following major natural disasters, such as devastating earthquakes, are crucial for responding to immediate impacts and conducting search-and-rescue operations. With the rapid advancement of unmanned-aerial-vehicle (UAV) technologies, UAVs have become an excellent choice for carrying sensing and detection systems, as they offer better accessibility to disaster-stricken areas that are difficult for rescue teams to reach. However, a major challenge is that the acoustic sensing systems on UAVs are often affected by strong ego and environmental noise, leading to extremely low signal-to-noise ratios (SNRs), typically well below 0 dB, which makes most DOA estimation and source localization algorithms ineffective. To address this challenge, this work explores the design of dipping microphone arrays carried by UAVs and the associated DOA estimation algorithms. The major contributions are threefold: 1) A dipping microphone array with a reconfigurable topology is designed, which significantly improves the SNR by adjusting the dipping length and enhances DOA estimation by configuring the array topology; 2) A maximum front-to-back ratio (MFBR) beamformer is developed to further mitigate the impact of UAV ego noise, further improving the SNR; 3) Building on the use of the dipping array and MFBR beamformer, a multiple-signal-classification (MUSIC) like algorithm is proposed to achieve accurate DOA estimation in challenging acoustic environments. Simulations and experiments are conducted to validate the effectiveness of the proposed design and algorithms.