Research On Unmanned Aerial Vehicles Detection Technology With Public Broadcasting Signals

In recent years,unmanned aerial vehicles(UAVs)have been widely used in military and civil fields,with the advantages of improving production efficiency,reducing human costs,developping new business types etc.,and showing huge market potential.However,the popularization of UAVs also means a reduction in the threshold for acquisition and use,which not only brings economic value but also leads to various security issues such as plane crashes,illegal aerial photography,cyber attacks,and even suicide attacks.UAV detection has become a research hotspot.According to the different energy sources,the existing UAV detection methods can be divided into two types of detection technologies depending on the direct radiation(radio frequency signal,acoustic wave,infrared ray)of UAV and the reflection(light wave,radio frequency signal)of UAV.This dissertation relies on the radio frequency signals of existing communication networks such as public broadcasting to discuss the UAV detection technology,focusing on the UAV detection methods with terrestrial digital TV broadcast signals,5G base station broadcast signals and full duplex base station signals reflected by UAV.Aiming at the practical problems faced by UAV detection under different public broadcast signals,this dissertation proposes corresponding solutions without occupying communication time-frequency resources,analyzes the theoretical performance of the proposed methods,and carries out some experimental verification,the contributions of which as follows:(1)The detection of UAV with digital terrestrial multimedia broadcast(DTMB)signals is studied.Firstly,the influence of UAV on the received signal is analyzed,and a method to detect the presence of UAV intrusion in the scene by extracting channel changes caused by UAV through high-order cumulants is proposed.The proposed method separates the stationary signal into constant coefficients by calculating the high-order cumulant,and extracts and amplifies the difference caused by the time-varying speed of the channel in the normal environment and the UAV intrusion.Then,the influence of cumulant order,normal channel fading speed and channel coefficient of UAV reflection path on the missed alarm probability and false alarm probability of high-order cumulant variance UAV detection method is analyzed.Finally,an experimental platform is built to test the detection probability and false alarm probability of the proposed UAV detection method for UAVs at different distances,as well as the ability to distinguish human and UAV,and verify its effectiveness.(2)The detection of UAV with 5G base station broadcast signals is studied.The 5G base station broadcast signal can be demodulated and then modulated to obtain a baseband reference signal,but also has the characteristics of non-uniform and discontinuous segmentation,which will affect the coherent integration.To solve this problem,the Doppler bias and cumulative gain loss of non-uniform discontinuous segmented signals used for conventional coherent accumulation are analyzed first.Two methods,namely non-uniform Fourier transform and phase iteration correction,are proposed to improve coherent integration,whose performance and computational complexity are analyzed.The simulation and experimental verification show that the proposed phase iterative correction method is superior to non-uniform Fourier transform and conventional coherent accumulation,and the UAV detection is realized by improving the coherent accumulation through the phase iterative correction of 64 5G synchronization signaling block(SSB)signals under the scenario of insufficient conventional coherent accumulation gain.(3)The geographical location optimization of the receiver for detecting UAV using5 G base stations is studied.Considering that UAV detection based on 5G broadcast signals is essentially a dual-base radar architecture,the geometric distribution of UAV detection performance under dual-base radar is analyzed to provide suggestions for the deployment of detection receivers.The performance analysis considers radial acceleration and path loss,and derives the influence of geometric variables related to the flight direction and geographic location of UAV on the detection performance.Finally,the theoretical analysis conclusion is verified by simulation.The results show that the bistatic radar has good detection performance near the baseline extension line,so the baseline extension line should pass through the center of the overdue detection area in radar deployment.(4)The detection of UAV with full duplex base station signals is studied.In a full duplex integrated sensing system,there is interference between the remote intended communication signal,UAV echo signal,and local static multipath signal.First,the typical power of each component in the received signal is compared,and the feasibility of parallel communication and UAV detection after interference suppression with adequate performance is analyzed.Furthermore,based on the existing research achievements in self interference suppression in the field of full duplex communication and clutter suppression in the field of radar,the time-domain and the frequency-domain interference suppression algorithms which are compatible with target detection and communication are proposed,respectively.The performance and computational complexity of these algorithms are analyzed and compared.Finally,through simulation,it is found that the mean difference method proposed in the time-domain method achieves similar performance to the classical extended cancellation algorithm with lower computational complexity.The frequency-domain methods are slightly better than the time-domain methods but relies on orthogonal frequency-division multiplexing(OFDM)modulation.This dissertation studies the UAV detection based on the broadcast signals of the existing communication network,involving the UAV detection algorithms,clutter interference suppression algorithms,detection performance analysis,and detection recevier layout optimization,with different broadcast public signals.The research provides a reference and insight for the construction of UAV detection networks based on existing communication network,which is of great significance in addressing the security and privacy issues brought about by UAVs.