不確定系統(tǒng)魯棒協(xié)方差交叉融合穩(wěn)態(tài)Kalman濾波器
doi: 10.11999/JEIT141515
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2.
(黑龍江大學電子工程學院 哈爾濱 150080) ②(黑龍江工商學院計算機科學與技術系 哈爾濱 150025)
基金項目:
國家自然科學基金(60874063, 60374026)
Robust Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems
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2.
(Electronic Engineering College, Heilongjiang University, Harbin 150080, China)
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摘要: 針對帶不確定模型參數(shù)和噪聲方差的線性離散多傳感器系統(tǒng)，基于極大極小魯棒估值原理，該文提出一種魯棒協(xié)方差交叉(CI)融合穩(wěn)態(tài)Kalman濾波器。首先，用引入虛擬噪聲補償不確定模型參數(shù)，把模型參數(shù)和噪聲方差兩者不確定的多傳感器系統(tǒng)轉(zhuǎn)化為僅噪聲方差不確定的系統(tǒng)。其次，應用Lyapunov方程證明局部魯棒Kalman濾波器的魯棒性，進而保證CI融合Kalman濾波的魯棒性，且證明了CI融合器的魯棒精度高于每個局部濾波器的魯棒精度。最后，給出一個仿真例子來說明如何搜索不確定參數(shù)的魯棒域，并驗證所提出的魯棒Kalman濾波器的優(yōu)良性能。
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關鍵詞:
- 多傳感器信息融合 /
- 不確定系統(tǒng) /
- 魯棒Kalman濾波器 /
- 虛擬噪聲 /
- 協(xié)方差交叉融合
Abstract: For the linear discrete time multisensor system with uncertain model parameters and noise variances, a Covariance Intersection (CI) fusion robust steady-state Kalman filter based on the minimax robust estimation principle is presented. Firstly, introducing the fictitious noise, the model parameter uncertainty can be compensated, so the multisensory system with both the model parameter and noise variance uncertainties is converted into that with only uncertain noise variances. Secondly, using the Lyapunov equation, the robustness of the local robust Kalman filter is proved, so the robustness of the CI fused Kalman filter is guaranteed and it is proved that the robust accuracy of the CI fuser is higher than that of each local filter. Finally, a simulation example shows that how to search the robust region of uncertain parameters and shows the good performance of the proposed robust Kalman filter. -
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