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Research On Radar Tracking Technique For Spatial Targets In The Presence Of Range Deception Jamming

Posted on:2016-03-20Degree:MasterType:Thesis
Country:ChinaCandidate:L ZhongFull Text:PDF
GTID:2348330536967680Subject:Electronic and communication engineering
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In the process of electronic countermeasures(ECM)and electronic counter countermeasures(ECCM),in order to improve the survival capability of targets,the stealthy technique has militarily extensive application.By using this technique,the reduce of targets Radar Cross Section(RCS)lowers the signal-to-noise(SNR),making rival radars has a hard choice in selecting a suitable Const False Alarm Rate(CFAR)threshold to detect targets.Consequently,targets can not be tracked steadily and continuously in the specified remote distance.At the same time,developed jamming system generates active decoys to confuse rival radar,making the radar defense system can not distinguish true targets from multiple active decoys.The exploitation of stealthy technique and false target jammings poses a great challenge to the detection and tracking ability of radar defense system.Furthermore,radar,as the vital weapon equipment in the military battlefield,whose detection ability,tracking ability and discrimination ability will influence the result of whole battlefield.Hence,it's very necessary to make research on the radar detection and tracking technique for spatial targets in the presence of complex electronic countermeasures.In this thesis,we have investigated some research on the issue of tracking stealthy target in the background of range deception jamming.The main contributions of the thesis are as follows:1.The influence mechanism of range false targets on radar detection,tracking and association system.Firstly the jamming principle of active decoys has been reviewed.Several detection approaches and the relative detection principle have been analyzed.Then the conceal effect of range false targets on the true target and the CFAR detection threshold have been illustrated by computer simulation.The target motion model and measurement model have been established in the presence of clutter environment and range deception jamming respectively,and the influence of range false targets on radar tracking and association system has also been verified.however,Simulation results have illustrated that the performance of probabilistic data association in the presence of range deception jamming is far less than that of nearest neighbor domain method instead due to the linear distribution of the range deception jamming.2.The technique of active high fidelity decoys.Aiming at dealing with exo-atmospheric penetration deception jamming,the analytic expression among the deception range of active decoys,the radar location parameters and the targets measurements parameters has been derived according to target kinematics characteristic of moment of momentum;A new trajectory jamming technique of the active high fidelity decoys has been proposed based on the kinematics modulation;By using the weighted least squares principle,the feasibility and validity of the proposed algorithm were verified.3.Joint tracking and discrimination algorithm based on ML-PDA method.Firstly the kinematics characteristic of radar target and active decoys have been analyzed in the twodimensional plane,and summarized the kinematics characteristic of true and false targets by using an uniformed mathematical model.The analytic expression of log likelihood ratio were derived based on the initialization parameters.According to the deception range component in the initialization parameters,a discrimination algorithm have been proposed,and the flowchart and detailed procedures of discrimination algorithm also have been presented.Simulation results have demonstrated that the proposed algorithm can detect and track weak stealthy targets precisely as well as discriminate the potential active decoys deception jamming.
Keywords/Search Tags:target tracking, range false targets, moment of momentum, track before detect(TBD), discrimination, log-likelihood rate(LLR), maximum likelihood probabilistic data association(ML-PDA)
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