The problem of maneuvering target tracking is a hot issue in the field of target tracking. Due to the range rate measurement containing the maneuvering information of target, it has the important practical significance to study how to use the range rate measurement to improve the effect of maneuvering target tracking. In the framework of interacting multiple model algorithm, the range rate measurement is used to update target state estimate and the probability of motion model to improve the tracking performance. As the measurement equation including the range rate measurement is strongly nonlinear, square root cubature Kalman filter algorithm is selected as the filter in interacting multiple model algorithm. The normal acceleration is deduced from the range rate with the reality constraint. And through Monte Carlo simulation, the empirical distribution functions of the normal acceleration statistics corresponding to different motion models are obtained. Their approximate distribution functions are obtained by the use of the expectation maximization algorithm with Gaussian mixture model. Then the probability distribution and probability distribution of measurement prediction residual are combined into a new likelihood function to improve the efficiency of updating the model probability. The experimental results show that the interacting multiple model algorithm proposed in this article has the smaller root mean square error of position and velocity and has the smaller average Kullback-Leibler divergence of model probability during the motion model stable phase.
KeywordsTarget tracking, range rate measurement, tracking and filtering, square root cubature Kalman filter algorithm, the normal acceleration statistic Date
The purpose of this paper is to track the air-to-air missile. Here we put forward the PN-GAPF (Proportional Navigation motion model and Genetic Algorithm Particle Filter) method to solve the problem. The main jobs we have done can be listed as follows: firstly, we establish the missile state space model named as the Proportional Navigation (PN) motion model to simulate the real motion of the air-to-air missile; secondly, the PN-EKF and PN-PF methods are proposed to track the missile, through combining PN motion model with EKF and PF; thirdly, in order to solve the particle degeneracy and diversity loss, we introduce the intercross and variation in GA to the particles resampling step and then the PN-GAPF method is put forward. The simulation results show that the PN motion model is better than the CV and CA motion models for tracking the air-to-air missile and that the PN-GAPF method is more efficient than the PN-EKF and PN-PF.
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