Research on drone recognition based on their acoustic emission using fully connected neural networks - Zubkov, O.V.; Boiko, N.V.; Machonis, T.S. (Radiotekhnika, 2025)
The relevance of the research on recognizing the self-acoustic emissions of drones using fully connected neural networks and cepstral coefficients has been substantiated. A dataset of acoustic recordings has been created, including self-emissions of various drone models, background sounds, and seven types of sound sources with spectral characteristics similar to drone acoustic emissions. The optimal number of cepstral coefficients has been identified for further recognition by fully connected neural networks in terms of maximizing the probability of correct recognition and minimizing misclassification. The optimal neural network architecture has been determined to ensure the highest probability of correct recognition. The requirements for a microprocessor-based hardware platform for recognizing drone self-acoustic emissions have been calculated.