Adaptive multiple model filter using IMM and STF

被引：0

作者：

Liang, Yan ^{[1
]}

Pan, Quan ^{[1
]}

Zhou, Dong-Hua ^{[2
]}

Zhang, Hong-Cai ^{[1
]}

机构：

[1] Dept. of Automatic Control, Northwestern Polytechnic University, Xi'an 710072, China

[2] Dept. of Automatic Control, Tsinghua University, Beijing 100084, China

来源：

| 1600年 / Chinese Soc Aeronaut Astronaut卷 / 13期

关键词：

Adaptive filtering - Computer simulation - Kalman filtering - Mathematical models - Modal analysis - Tracking (position) - White noise;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least-Squared Estimation. In hybrid estimation, the well-known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter - SIMM. In this filter, our modified STF is a parameter-adaptive part and IMM is a mode-adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model-conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.

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