Passive Drone-to-Drone Acoustic Bearing Estimation Based on Statistical Self-Noise Signatures

The increasing proliferation of unmanned aerial vehicles (UAVs) has created a growing need for passive sensing techniques capable of operating in dynamic environments. This paper presents a passive drone-to-drone approach in which an observer UAV equipped with a microphone array captures the sound emitted by a target drone during flight. A statistical harmonic signature of the observer’s own platform is first constructed from repeated multichannel recordings and used for selective self-noise (ego-noise) suppression; the preserved tonal components of the target are then processed by multichannel time-delay estimation to obtain its azimuth relative to the array. Experiments on the AIRA-UAS dataset show consistent suppression of the selected observer-UAV harmonics (10.49–11.84 dB) with limited modification of the non-targeted broadband content, and produce azimuth sequences that are temporally stable and consistent with the expected motion of the target. As the dataset does not provide bearing ground truth synchronized with the audio, the study is presented as a feasibility assessment of self-noise suppression and bearing-trend estimation rather than of absolute localization accuracy, highlighting the potential of airborne acoustic sensing as a low-cost, passive component for UAV awareness applications.