Research on Sound Recognition of Long-Distance UAV Based on Harmonic Features

With the extensive application of unmanned aerial vehicles (UAVs) in both military and civilian domains, the significance of UAV identification technology has become increasingly prominent. Among various recognition methods, voice recognition has garnered considerable attention due to its advantages of low cost and easy deployment. However, most existing research primarily focuses on isolating UAV sounds from noise signals in complex environments, with limited studies on long-distance UAV sound recognition. Based on this, this paper proposes a frequency domain feature extraction method based on harmonic features. By analyzing the harmonic features of UAV sounds, we select stable parameters with strong robustness against interference capabilities as the main features to minimize information redundancy and feature fluctuation. The experimental results indicate that this method achieves a recognition accuracy of 78.03% for the DJI Phantom 4 Pro V2.0 UAV at a distance of 120 m. To validate the proposed method, comprehensive comparisons against traditional MFCC, Log-Mel Spectrogram, and modern Raw Waveform CNN (M5) baselines demonstrate the superior robustness of the proposed approach. While these comparative methods exhibited significant performance drops in challenging long-distance scenarios (e.g., accuracies falling below 24% for the DJI Mavic Pro), the proposed method maintained consistent identification capabilities, validating its effectiveness in low-signal environments.