A Study On Tracking The Slow Moving Targets With Low Resolution Radar

Radar is an essential electronic device in modern warfare,and target tracking is an important function of radar equipment.Its tracking performance directly affects radars' performance.Therefore,the target tracking technique is the focus of attention to the researchers.For the slow moving targets with low resolution radar,the measurement error is often large,which greatly degrades the tracking performance.In this thesis,the key techniques of tracking the slow moving targets with low resolution radar are discussed,and the target motion model and target tracking algorithm are analyzed in detail and the simulation researches are conducted.The main contents include: Firstly,the basic knowledge of target tracking is introduced.Combined with some coordinate systems commonly used in radar tracking systems,several classical target motion models including CV(constant velocity)model,CA(constant acceleration)model,CT(constant turn)model,Singer model and Jerk model are described,and the scope of application and characteristics of each model are discussed in depth.Secondly,the filtering method of the moving targets is discussed.First,the Kalman filter algorithm is introduced.Then the Extended Kalman Filter(EKF)algorithm,Unscented Kalman Filter(UKF)algorithm,Cubature Kalman Filtering(CKF)algorithm,Partial Filter(PF)algorithm,Gaussian sum KF with colored noise algorithm and Interactive Multiple Model(IMM)algorithms are studied.Finally,based on the radar measurement model,the filtering algorithm is simulated and compared on the basis of the target motion model.Thirdly,a filtering method for tracking slow moving targets with low-resolution radar is studied.Aiming at the low tracking accuracy caused by large measurement error of low-resolution radars,an adaptive Kalman filter method by covariance sampling was studied.This method approximates the measurement noise covariance distribution through using a finite sample set,assuming the noise to be white with a normal distribution.Exploiting the sample set in approximation of the system state a posteriori leads to a Gaussian mixture model(GMM)that used to estimate the measurement noise covariance to enhance the tracking accuracy.At the same time,a Kalman filter method based on finite-step memory is proposed.The key of this method is to combine the information of the past few steps and the information of the current moment to estimate the state of the next moment,which solves the problem of low tracking accuracy caused by large measurement error.Finally,simulation experiments verify the effectiveness of the two methods.