Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement. (December 2017)

• Record Type: Journal Article
• Authors: Liu, Hongqiang; Zhou, Zhongliang; Yang, Haiyan
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 12(2017)
• Publication Date: 2017-12
• Subjects: Target tracking -- range rate measurement -- tracking and filtering -- square root cubature Kalman filter algorithm -- the normal acceleration statistic; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717747848
• Languages: English
• ISSNs: 1550-1329

Abstract

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.