Research On Data Association Method Of Anti-pull Off Deception Jamming

Pull off deception jamming can destroy the process of data association in radar tracking,meanwhile it can realize the traction of the tracking prediction gate,affect target tracking accuracy and affect the data association performance of plot-to-plot or plot-to-track association by generating distance-dimensional deception information.So,anti-pull off jamming is a frontier problem that needs to be solved urgently in the field of electromagnetic countermeasures at home and abroad.The existing anti-pull off jamming methods usually distinguish and filter out the jamming measurement based on the signal characteristics of the echoes,which is difficult to deal with the increasingly refined and intelligent pull off deception jamming threats,which greatly reduces the radar tracking performance.However,using the information of pull off deception jamming at data level to improve the traditional data association algorithm is conducive to suppressing the influence of pull off jamming and improving the performance of target tracking in complex electromagnetic environments.Based on the analysis of the mechanism of traditional data association,this thesis focuses on the data association methods for anti-pull off jamming in single-target and multi-target tracking scenarios.The main work of this thesis is as follows:1.Aiming at the problem that the pull off deception jamming destroys the traditional data association process,this thesis studied the influence of pull off deception jamming on the target track and tracking accuracy,when the targets of different motion models are tracked by the traditional data association algorithm.2.In response to the statistical characteristics of the pull off deception jamming in the spatial distribution,through thex~2 statistical test method combined with the M/N criterion,this thesis studied the calculation of the association probability of target tracking in a complex electromagnetic environment,and it provided a theoretical basis for the follow-up research on data association methods of anti-pull off deception jamming.3.In view of the problem that the existing single-target tracking algorithm is difficult to deal with the pull off deception jamming,this thesis studied the single-target anti-pull off jamming based on the Probabilistic Data Association Algorithm and the track splitting method according to the distribution characteristics of the pull off jamming.Meanwhile,this thesis used the tracking performance indicators such as Minimum Mean Square Error and Tracking Error Cumulative Probability Distribution,verified the effectiveness of the algorithm in terms of tracking accuracy and pull-off success rate through simulation experiments.4.To solve the problem that multiple tracks compete for pull-off jamming measurements and affect the tracking performance in multi-target tracking scenario,according to the distribution characteristics of pull off jamming,this thesis has constructed a multi-target tracking model and studied the data association algorithm of anti-pull off jamming based on Joint Probabilistic Data Association Algorithm and Gaussian Mixture Probability Hypothesis Density Algorithm.Meanwhile,this thesis used the tracking performance indicators such as Minimum Mean Square Error and Optimal Sub Pattern Assignment Distance,verified the effectiveness of the algorithm in terms of tracking accuracy.The above methods are verified through theoretical analysis and simulation experiments.The analysis results show that the above methods can effectively suppress the effect of pull off jamming on the performance of target tracking.