Maneuvering target tracking by adaptive statistics model

• Published in: Journal of China universities of posts and telecommunications Vol. 20; no. 1; pp. 108 - 114
• Main Authors: JIN, Xue-bo, DU, Jing-jing, BAO, Jia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2013
• Subjects: Acceleration; Autocorrelation functions; China; maneuvering target; Maneuvering targets; Mathematical models; On-line systems; state estimation; Statistics; statistics relation; target model; Tracking; 估计性能; 加速模型; 机动目标跟踪; 电流模型; 统计模型; 自适应模型; 跟踪机动目标; 马尔可夫模型; state estimation; maneuvering target; target model; statistics relation
• ISSN: 1005-8885
• DOI: 10.1016/S1005-8885(13)60016-3

A good model can extract useful information about the target＇s state from observations effectively. There are many models used to tracking a, maneuvering target such as constant-velocity （CV） model, Singer acceleration model （zero-mean first-order Markov model） and current model （mean-adaptive acceleration model）, etc. While due to the complexity of maneuvering target, to seek the target model which can get better performance is still a subject worthy of study. Based on statistics relation between the autocormlation function and the covariance of Markov random processing, this paper develops a model which can adaptively adjust system parameters on line. Simulations show the good estimation performance get by the model developed here, and comparing CV, Singer and current models, the model can adaptively get the model parameter while tracking the trajectory and needn＇t doing several tests to obtain a priori parameter.