Research On UAV Signal Detection And Recognition Method Based On Data Aided

In recent years,Unmanned Aerial Vehicle(UAV)have been widely used in various military and civilian scenarios,and many security issues have also emerged.Faced with the urgent need to supervise UAVs,radio detection technology,as an effective detection method,can detect and identify targets by passively detecting UAV signals.But most UAV communication protocols are kept private as trade secrets,making it harder to detect and identify UAV signals.Therefore,in the signal analysis stage,this thesis obtains the synchronization sequence in the signal by blindly demodulating the UAV signal.Then,the data-aided UAV signal detection and recognition algorithm is studied.The main work and innovations of the article are as follows:(1)The time-frequency analysis and blind demodulation of civilian UAV signals are carried out.The peripheral parameters of UAV uplink and downlink signals are obtained through time-frequency analysis,and the UAV detection and identification system is designed based on the analysis results.In terms of blind demodulation of UAV signals,the parameter blind estimation and blind timing synchronization algorithms are optimized to improve the performance of the algorithm and meet the blind demodulation requirements for UAV short burst signals.Therefore,the synchronization sequence in the signal can be obtained correctly,which lays the foundation for the subsequent research on the UAV signal detection and identification algorithm based on data assistance.(2)The Orthogonal Frequency Division Multiplexing(OFDM)UAV signal detection algorithm based on data-aided is studied.Corresponding signal detection algorithms are designed for different UAV synchronization sequences.Based on the synchronization sequence of WiFi UAV signal,the theoretical detection probability and false alarm probability of the multi-layer differential correlation algorithm are deduced.Compared with the traditional single-layer autocorrelation detection algorithm,the detection performance under low signal-to-noise ratio is improved.Based on the synchronization sequence of Phantom 4A UAV signal,the theoretical detection probability and false alarm probability of the normalized delay autocorrelation algorithm are deduced and verified by simulation.Based on the synchronization sequence of Mavic2 UAV signal,the frequency offset sensitivity of traditional algorithm is analyzed,and an improved algorithm with anti-frequency offset performance is proposed,which improves the application range of the detection algorithm.(3)The single-carrier UAV signal detection algorithm based on data-aided is studied.Some large UAVs,especially military UAVs,communicate and control through specific satellite single-carrier links.Based on the centralized unique word,the principle of the traditional likelihood ratio double correlation detection algorithm is analyzed,and the algorithm is optimized.Under the condition of sacrificing part of the anti-frequency offset range,the computational complexity of the algorithm is reduced,and the detection accuracy is basically unchanged.For distributed unique word the performance of existing burst detection algorithms is analyzed,and an improved algorithm is proposed.The detection accuracy and robustness of the algorithm are improved,and the detection algorithm is suitable for distributed unique word with different distribution structures.In summary,this thesis blindly demodulates the UAV signal in the signal analysis stage to obtain the synchronization sequence.And based on the special structure of the synchronization sequence,the corresponding signal detection algorithm is proposed.In addition,there are often differences in the synchronization sequence structure of different types of UAVs.When the signal detection is completed based on the synchronization sequence,the identification of the UAV type is also realized.The simulation results show that the proposed data-aided detection algorithm has good antifrequency offset performance and high detection accuracy under the condition of low signal-to-noise ratio.