Research On Key Technologies Of Passive Positioning For UAV

With the development of social economy,the ”low,slow and small” targets represented by drones have become a growing security threat in the air,and existing conventional weapons and equipment are generally unable to effectively intercept them.Considering that the traditional active radar is not suitable for the complex electromagnetic environment of the city,and the radar cross-sectional area of the UAV is difficult to capture,the passive positioning technology becomes the first choice for the positioning of the UAV.The passive positioning observation device does not emit electromagnetic waves outward,but only passively accepts external radio signals to locate the target.At the same time,the passive positioning has the advantages of large coverage,low cost,good concealment and so on.This paper mainly studies the passive positioning and tracking technology based on the UAV target.Firstly,the paper introduces the main classification and technical characteristics of passive location and security threats brought by the development of UAVs.Then it describes the research status of UAV detection and interference methods.Secondly,the thesis introduces the theory of link structure,communication principle and signal parameter characteristics of the UAV communication system.Through comparison of different UAV platform products,the DJI Phantom 4 Pro UAV is selected as the research object.The paper focuses on the key technologies,performance indicators and time-frequency domain analysis methods of frequency hopping communication in UAV communication system.The time-frequency domain simulation of frequency hopping signals is carried out,which provides technical foundation for the research on passive positioning technology of UAVs.The paper designs an experiment to collect the real signal data of the drone,and analyzes the time-frequency domain characteristics of the remote control signal and the image transmission signal of the drone.Then the paper,based on the characteristics of UAV signals,proposes a passive positioning system for drone based on angle and time difference of arrival(TDOA)cooperative estimation.Firstly,the two measurement parameters are estimated separately.On the one hand,based on the UAV OFDM signal model,a multi-carrier TDOA estimation method based on cyclic prefix(CP)blind estimation is proposed.The mini-mean is used as the feature.The value is extracted and arranged in the main receiver.Compared with the traditional standard centralized method,the performance of the partially decentralized cross-correlation TDOA estimation method greatly reduces the data transmission requirements between stations,improves the TDOA estimation speed,and reduces the estimation error probability.On the other hand,the paper analyzes the basic multiple signal classification(MUSIC)algorithm and the decoherent MUSIC algorithm for angle measurement,and solves the problem of the rank coherence matrix of the received source covariance matrix due to multipath or interference in practical applications.The paper comprehensively studies a joint estimation algorithm based on pseudo-spectral method(PM)for delay and angle.Finally,based on the premise of TDOA estimation algorithm,a TDOA localization algorithm based on grey wolf optimization is proposed.The individual learning strategy is added to the standard grey wolf optimization algorithm,and the trigonometric nonlinear convergence factor is used to balance the detection performance and evolution performance.The paper proposes a partial decentralized TDOA localization method based on CP blind estimation.Positioning the CP in each received data stream,and then solving the integer fuzzy problem in TDOA positioning by utilizing the underlying positioning problem,and deducing the Cramer-Rao lower bound(CRLB)with the accuracy of TDOA positioning by CP.The paper simulates the effects of different factors(number of data blocks,signal-to-noise ratio,CP length,multipath effect,etc.)on CRLB and root mean square error(RMSE)of TDOA positioning.The proof algorithm realizes the distribution calculation processing of TDOA data,reduces the data sharing burden,accelerates the positioning speed,and improves the performance of the whole system.The paper analyzes the cross-positioning technology and analyzes the GDOP accuracy of the direction-finding method through simulation.