Research On Monopulse Estimation And Angle Tracking Method In Passive Radar

Passive radar mainly uses the third-party illumination source to detect the target to obtain its angle and perform angle tracking,which does not actively transmit signals.Therefore,passive radar has the advantages of strong anti-interference ability and high concealment,which has been fully used and comprehensively promoted in modern warfare activities such as anti-stealth,anti-low-altitude penetration,enemy target detection and positioning,it has been widely concerned in related fields in China and abroad.This paper mainly focuses on the field of passive radar target direction finding and angle tracking,aiming at the problem that traditional monopulse estimation is easily affected by mainlobe and sidelobe interference,which leads to the decline of monopulse performance,and traditional filtering angle tracking algorithm has some problems such as low tracking accuracy for complex maneuvering target.Corresponding improvement methods are proposed,and the proposed algorithm has good performance through theoretical analysis and experimental simulation.The specific research contents are as follows:1.In response to the problem of traditional sum-difference monopulse estimation is easily affected by sidelobe interference,which leads to the decrease of angle measurement accuracy,a low-sidelobe adaptive sum-difference monopulse estimation is proposed.The method firstly uses the Taylor weight to process the sum beam,and uses the Bayliss weight to process the difference beams to form a low sidelobe sum-difference beam by direct weighted algorithm;and then uses a beamforming method based on minimum variance distortionless response(MVDR)criterionto perform adaptive weighting processing of the quiescent sum-difference beam output,so that it maintains good directivity in the beam pointing direction,and can generate "zero trap" at the sidelobe interference position to suppress sidelobe interference and improve the problem of serious distortion of the sum-difference beam patterns.It also improve angle estimation accuracy.2.In view of the problem that low sidelobe adaptive sum-difference monopulse estimation will lead to the deterioration of adaptive beamforming performance and serious distortion of the monopulse ratio curve in the presence of mainlobe interference,a robust adaptive monopulse estimation based on two-step covariance matrix reconstruction is proposed.The algorithm completes the reconstruction of the interference-plus-noise covariance matrix by minimizing the interference-plus-noise power output criterion and the uncertainty set constraint of the interference steering vector.Then,the azimuth and elevation are jointly linearly constrained according to the set interval.This method effectively solves the problems of poor interference suppression ability and low angle measurement accuracy of traditional adaptive monopulse estimation,it also improves the problem of beamforming performance deterioration caused by the mismatch between steering vectors and covariance matrices,and improves the robustness of the algorithm.3.Aiming at the problem that traditional angle tracking algorithm based on kalman filter will appear filter divergence when the system nonlinearity is strong,which leads to the decrease of angle tracking accuracy,and considering that the angle of maneuvering target changes rapidly and complicatedly,which can’t be accurately described by single mathematical model.An angle tracking algorithm based on interacting multiple model cubature kalman filter(IMM-CKF)is presented.Firstly,the algorithm combines the robust adaptive monopulse estimation proposed in this paper to accomplish the target direction finding and obtains angle measurement values of the target at different time to construct the system state equation and measurement equation.The switching between each model is controlled by the probabilistic transfer matrix,and each model is filtered in parallel through cubature Kalman filter algorithm.Finally,the state prediction is completed through time update and measurement update,and then the filtered state is processed by model probability update to achieve output fusion,which can better solve the problem of nonlinear filter divergence,and further improve angle tracking accuracy and performance.