UAV acoustic recognition in complex environments based on attention mechanism fusion and variational mode decomposition
With the rapid development of unmanned aerial vehicle (UAV) technology, unauthorized small-drone flights are increasing, threatening civil aviation and public safety. Since radio frequency, electromagnetic and vision-based methods can be unreliable-especially in fog-acoustic UAV identification is a practical alternative. This study proposes a deep-learning method for UAV sound recognition and tests its real-world performance by mixing UAV audio with diverse background noises at multiple signal to noise ratio (SNR) levels to simulate realistic conditions.This study uses Mel-spectrograms as the extracted features for UAV audio recognition. By parallelly fusing squeeze-and-excitation networks and coordinate attention mechanisms, an improved deep learning model, ResNet18_Attention, is proposed based on the traditional ResNet18, which effectively enhances the feature representation capability. At the same time, the variational mode decomposition filtering algorithm is employed as a pre processing step to perform noise suppression before feature extraction, further improving the recognition and classification performance of UAVs under low SNRs. This study conducts a comparative evaluation between the proposed improved model, a model enhanced solely with a single attention mechanism, and four conventional baseline models. The pro posed model achieves consistent improvements in accuracy, precision, recall, and F1-score under noisy conditions. In addition, a filtering algorithm is employed to preprocess the UAV audio data.Experimental results show that, after filtering, the improved model achieves an average accuracy that is 15.1% higher than that of the ResNet18 model without filtering within the experimental SNR range, and the final average accuracy reaches 98.6% over this SNR range.