{'fraction_non_alphanumeric': 0.02018348623853211, 'fraction_numerical': 0.005504587155963303, 'mean_word_length': 5.163841807909605, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research on UAV Signal Classification Algorithm Based on Deep Learning

∗With the continuous development of Unmanned Aerial Vehicle (UAV) technology and its industry, the detection and recognition technology of UAV have attracted the attention of researchers. In this paper, the author focuses on the defects and deficiencies of traditional radar, visual and acoustic UAV detection technology. Considering that the UAV’s own radio communication signal can be used for detection, a UAV signal classification method based on deep learning is proposed. This algorithm can extract the characteristics of UAV Communication Law, so as to achieve the target classification. The experimental results show that the average recognition rate of UAV is 95% in the test, and the recognition rate of most types of UAVs is more than 98%. In addition, the classification rate for the flight attitudes of UAVs can reach more than 95%. Therefore, it can be concluded that the classification algorithm designed in this paper can effectively meet the needs of UAV detection and recognition in the actual scene.