Study On Motion Model And Tracking Algorigthms Of Radar Maneuvering Target

With the change and development of tracking target, the study of target tracking iscontinuously and deeply developed. Through target tracking, accurate estimation oftarget state is gotten, and then a large amount of subsequent information processing canbe realized, such as target threat estimation, command decisions, and so on, which arebased on stable tracking data of the target. For the emergence of new tracking targetsand the increasing information requirement of target tracking, maneuvering targettracking is more and more becoming the current research hotspot. Combined with the863project:“the research of xxx aerospace multi-source information”, this dissertationmainly studies the motion model and tracking algorithms of radar maneuvering target.The main contents of the dissertation include:Firstly, research background of this dissertation is introduced, then two keyproblems in maneuvering target tracking are discussed detailly, which include targetmotion model and tracking algorithms, and the research contents of this dissertation areintroduced.The motion model of strong maneuvering target is studied, which is based on “α”and “η” parameters. Based on detail analysis of Singer model and Jerk model, someshortages of Singer model and Jerk model in the characterization of target motioncharacteristics are pointed out. Based on this, α-η parameter motion model of strongmaneuvering target is built, which is using maneuvering frequency α and jerkingfrequency η as parameter characteristics. Through discretization processing for α-ηparameter motion model, the state-measurement model of α-η parameter motion modelis deduced, and the characteristics of α-η parameter motion model are analyzed in detail.The experimental results show that the proposed motion model is effective for the targetmaneuvering model representation.A kind of target tracking algorithm based on improvedly unscented kalmanfilter(MUKF) is proposed. In the UKF algorithm, filtering gain calculation is mainlydecided by two variances: state covariance and measurement covariance, filter gain willlag behind the target maneuvering state when the target maneuvers, so that the trackingerror is bigger. Therefore, in the process of tracking, the scale factor of state noisecovariance is estimated in every tracking step of UKF, which is used to modify theforecast state covariance, the state covariance is updated by forecast state covariance,then filter gain is modified. The filter gain is got by modified state covariance using adaptive scale factor, which causes the filter gain matches maneuvering state of thetarget, then the better tracking accuracy is got. The experimental results show that thetracking performance of the proposed method is more accurary than that of UKF.Based on the advantage of unscented transform(UT) and extended kalmanfilter(EKF), two algorithms of target tracking are proposed, which aims to improvetracking performance and decrease operation time of algorithm.(1) A new targettracking algorithm based on unscented extended kalman filter(UEKF) is studied. Innonlinear tracking system, UKF has better tracking performance than EKF, but thecomputational time of UKF is greater than that of the EKF. For these reasons, a newmethod for tracking maneuvering target is put forward, which is to combine the UT withthe EKF. The key idea is to transform multi-vector multiplying into the addition ofmulti-vector, which causes the operation time of new algorithm is much less than that ofthe UKF. The UEKF is to combine the diversity of sigma particle with the lessoperation time of EKF, which causes that not only the better tracking presion but alsothe less operation time is kept.(2) An adaptivly unscented extended Kalmanfilter(AUEKF) algorithm is studied. In course of UEKF, the two covariances of noisebased on the exponential attenuation and forgetting factor are estimated, which is basedon the residual information of filter, so the covariance of noise is adaptively estimated.The experimental results show that the tracking presion of the two kinds of algorithmsis better than that of UKF, but also has less operation time.Based on modified model probability, a kind of interacting multiple modelalgorithm is brought forward, which aims to improve model probability estimationaccuracy. In calculating course of the interacting multiple model algorithm, the sateweighting factor(or model probability) is calculated by covariance of residualinformation and predicted value of model probability, but information of current statecovariance isn’t effectivly used in IMM algorithm, which causes that the modelprobability estimation isn’t accurate. For these reasons, the new scale factor based onthe current state covariance is studied, then the state weighting factor is modified by thenew scale factor. Both the predicted model probability factor and the scale factor of thecurrent state covariance are used, as a result, the relatively precise model probabilityestimation is got. The experimental results show that the model probability estimationof the proposed method is more accurate than that of IMM. At last, this paper’s work is concluded, and the shortage of paper is pointed out,research directions in the future are discussed.