Grouping Parallel Detection Method of UAV Based on Multi Features of Image Transmission Signal

Abstract

The emergence of low, slow, and small civilian unmanned aerial vehicles (UAV) brings fun and convenience to life and work. However, with the widespread popularity of UAV, the illegal activities caused by them have gradually increased, causing great harm to social security. To solve this problem, in the paper, we propose a set of detection and recognition methods for UAV by UAV image transmission signal (ITS). The method is divided into two groups. In the first group, according to the signal characteristics in different transform domains such as spectrum and time-frequency spectrum, three sets of algorithms are proposed, which are time-frequency ridge double feature estimation (TFRDFE), segmented spectrum estimation (SSE) and cycle accumulation estimation of segmented spectrum (CAE-SS). Three sets of algorithms are estimated to perform blind detection on suspected UAV ITS. The second group uses the accurate recognition algorithm of UAV ITS to extract the periodic features in the signal, and completes the recognition of UAV through feature matching, decision criteria and other methods. The two groups of methods are implemented in parallel, and when the two groups both detect and recognize the flying target, it can be determined that there is UAV in the target airspace. The experimental results show that the recognition rate of the first group of suspected UAV ITS blind detection algorithm can reach 100% when the (signal-to-noise ratio) SNR is –22 dB. The second group of UAV ITS recognition algorithm can achieve 100% recognition rate when SNR is –4 dB. Therefore, this method can complete the multi-target recognition of UAVs and has practical application value.