Propulsion Noise Mitigation for UAV-Based Acoustic Target Detection: Spectral Characterization, Filter Optimization, and Real-Time Embedded Implementation

Abstract

Acoustic sensing enables unmanned aerial vehicles (UAVs) to detect sound-emitting targets beyond the visual field, particularly in environments where visual sensing is degraded by darkness, fog, occlusion, or camouflage. However, strong self-generated propulsion noise produced by onboard motors and propellers often masks external acoustic signals, limiting the applicability of UAV-based acoustic perception systems. This study investigates the spectral characteristics of UAV propulsion noise and evaluates a filtering-based approach for improving the observability of external acoustic targets. Acoustic measurements were conducted using a hexacopter UAV under controlled indoor conditions. The experimental dataset comprised recordings collected under motor-only, propeller-attached, and helicopter-noise mixture scenarios across multiple flight modes and source-distance conditions in which helicopter sounds were introduced through a loudspeaker. Signal analysis was performed using waveform inspection, Fourier-based spectral analysis, and spectrogram-based time–frequency representations. The results show that the dominant energy of UAV propulsion noise is concentrated in the low-frequency region of the spectrum. Based on this observation, a fourth-order Butterworth high-pass filter architecture was implemented and systematically evaluated using a benchmark sweep from 250 Hz to 400 Hz in 10 Hz increments. Within the evaluated range, the 340 Hz configuration achieved the highest helicopter top-1 classification accuracy of 62.86%, whereas unfiltered recordings and generic baseline denoising methods yielded 0.00% accuracy. Additional experiments conducted under multiple source-to-UAV distance conditions further revealed the influence of acoustic propagation on propulsion-noise masking behavior. Overall, the findings demonstrate that spectral characterization combined with computationally efficient high-pass filtering can effectively mitigate low-frequency UAV propulsion noise and enhance acoustic target observability. The proposed framework provides an effective preprocessing stage for UAV-based acoustic perception systems and establishes a practical foundation for future UAV-based acoustic target detection and multimodal perception systems. Although helicopter signatures were used as the target class in this study, the proposed preprocessing framework is not fundamentally limited to helicopter detection and may be applicable to other acoustically detectable targets. While the present study identifies an empirically optimal fixed cutoff frequency of 340 Hz for the evaluated platform and operating conditions, future work will investigate real-time adaptive filtering strategies capable of dynamically adjusting filter parameters according to propulsion-noise characteristics and flight conditions.