Static Ground Validation of an AI-Assisted Acoustic Target Detection and Azimuth Estimation Framework on a Flying-Wing VTOL UAV

Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness.