Detection And Recognition Method Of UAV Communication Signal Under Low SNR

In recent years,with the rapid development of civilian micro-UAVs(unmanned aerial vehicle),many safety issues have occurred.The potential safety hazards brought by the UAVs without approval have attracted attention from many parties.In fact,the premise of UAV supervision is the detection and identification.Passive detection only needs to receive the signal emitted by the drone,which will not affect the surrounding electromagnetic environment.Therefore,it is one of the effective measure of anti-UAV detection.As a non-cooperative receiver,it is necessary to perform detection,parameter estimation,modulation recognition and protocol recognition on the UAV communication signals.However,the low power of the signal transmitted by the civilian micro-UAV leads to weak signals received by the long-distance receiver.What’s more,the complex electromagnetic environment of city leads to noise interference and serious co-frequency interference.Therefore,it is necessary to conduct research on weak UAV communication signal under low signal-to-noise ratio(SNR).The main research contents are as follows:1.The common civil UAV communication protocols on the market are classified,and the UAV signals of Wi-Fi system and non-Wi-Fi system are analyzed systematically.It is concluded that the center frequency of the UAV communication signal in the Wi-Fi system overlaps with the Wi-Fi channel standard in China.The signal is bursty,which follows the public Wi-Fi protocol frame format and can be identified by decoding.The non-Wi-Fi system UAV communication signal has stable time-frequency characteristics,and different UAVs can be detected and identified according to the time-frequency parameter estimation.2.In order to detect the UAV signal,it is significant to research on the time-frequency parameter estimation method.Therefore,we propose a time-frequency parameter estimation method of UAV communication signal based on image morphology under low SNR.Firstly,using image morphology,median filtering,and two-dimensional adaptive filtering methods to process the signal time-frequency grayscale map.Secondly,selecte the best combination of structural elements through the analysis of structural elements.Finally,using Otsu binarization to optimize the signal decision threshold.After analyzing the frequency hopping simulation signal and actual UAV signal,the algorithm has better parameter estimation effect under low SNR.3.The signal of Wi-Fi UAV system and other civilian Wi-Fi have similar time-frequency characteristics.Hence the public Wi-Fi protocol is used to decode the UAV communication signal of Wi-Fi system for identification.Taking the IEEE802.11 a protocol as an example to introduce the signal frame format and the key steps of UAV signal decoding.Focusing on frame synchronization,this paper optimizes the parameters and proposes an improved algorithm of delay correlation algorithm.Furthermore,actual data was used to verify the UAV signal decoding.Experiments show that the improved delayed autocorrelation frame synchronization algorithm can detect the start position of the signal frame accurately under low SNR.And the decoding can obtain the parameter information of the UAV,which provides a strong basis for UAV identification.4.According to the needs of anti-UAV supervision,a UAV signal detection and identification test system is designed.The software and hardware environment of the system development is introduced.Furthermore,the main functions and implementation processes of the UAV signal time-frequency information module,the UAV signal detection and time-frequency parameter estimation module,and the UAV signal decoding and identification module of the system are described.The performance of the system is tested by using the simulated signal and the measured signal,and the experiment verifies the validity and practicability of the system in detecting the UAV signal and identifying the modulation mode of the signal.