Iterative algorithm for nonlinear dynamic filters

被引：0
|
作者：
Zhao, Changsheng [1 ]
机构：
[1] School of Geodesy and Geometrics, Xuzhou Normal University, Xuzhou 221116, China
来源：
Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing | 2007年 / 22卷 / 04期
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学科分类号：
摘要：
The algorithm of iterative Kalman filter is presented based on the general Kalman filter and the extended Kalman filter. The approximate processing methods for nonlinear dynamic filters are discussed. The nonlinear model is linearized for the nominal state system and the general Kalman filters is extended to the nonlinear model. Finally the extended Kalman filter formula is derived and the iterative algorithm is established. The extended Kalman filtering method has been effectively used in the nonlinear model.
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页码：431 / 434
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