Maneuvering target tracking by adaptive statistics model

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 accel...

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Bibliographic Details
Published inJournal 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
LanguageEnglish
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
Online AccessGet full text
ISSN1005-8885
DOI10.1016/S1005-8885(13)60016-3

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Summary: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.
Bibliography:11-3486/TN
maneuvering target, target model, statistics relation, state estimation
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.
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SourceType-Scholarly Journals-1
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content type line 23
ISSN:1005-8885
DOI:10.1016/S1005-8885(13)60016-3