Comparison of JML, EKF and SDDRE Filters of Nonlinear Dynamic Systems

被引：0
|
作者：
Rusnak, Ilan [1 ]
机构：
[1] RAFAEL 630, POB 2250, IL-3102102 Haifa, Israel
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON THE SCIENCE OF ELECTRICAL ENGINEERING (ICSEE) | 2016年
关键词：
JML; EKF; SDDRE; Comparison; Optimal filterin; nonlinear estimation;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The Joint Maximum Likelihood filter, also called Least Mean Square Error filter, has been recently derived for the state estimation of nonlinear dynamic systems. It is an exact, explicit, closed form and recursive optimal filter. In this paper performances of this new Least Mean Square Error filter, the Extended Kalman Filter and the State Dependent Differential Riccati equation based Filter are compared. The Van der Pol equation is used in this comparison. It is demonstrated by simulations that although the performances are not so different that the Least Mean Square Error filter is the fastest filter to reach for the first time zero tracking error.
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页数：5
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