Drone Detection Method Based on the Time-Frequency Complementary Enhancement Model

Drone audio detection methods have become a key component of anti-drone systems. Traditional audio feature extraction methods have problems such as large fluctuations in feature vectors, fixed extraction resolution, and redundant feature information extraction. Moreover, the dataset is ideal and not representative of real application scenarios. In this study, a dual-domain audio feature extraction method in the time and frequency domains is proposed that improves the accuracy of drone detection by combining the more richly detailed information in the time domain and the relatively stable property of the signal in the frequency domain. A real-world sound dataset that contains low signal-to-noise ratio (SNR) audio was collected for experimental validation. The results showed that, compared with existing methods, the proposed method took full advantage of the "zoom" feature of the wavelet packet transform (WPT), the local feature extraction capability of a 1-D convolutional neural network (CNN), and the global modeling capability of a self-attention mechanism, thereby effectively improving the success rate of drone detection in common scenarios. The proposed method also outperformed other methods with respect to several evaluation metrics.