不确定系统鲁棒协方差交叉融合稳态Kalman滤波器

王雪梅; 刘文强; 邓自立

doi:10.11999/JEIT141515

不确定系统鲁棒协方差交叉融合稳态Kalman滤波器

doi: 10.11999/JEIT141515

2.
(黑龙江大学电子工程学院哈尔滨 150080) ②(黑龙江工商学院计算机科学与技术系哈尔滨 150025)

基金项目:

国家自然科学基金(60874063, 60374026)

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- 被引次数: 0
出版历程
- 收稿日期: 2014-11-27
- 修回日期: 2015-03-27
- 刊出日期: 2015-08-19

Robust Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems

2.
(Electronic Engineering College, Heilongjiang University, Harbin 150080, China)

摘要

摘要: 针对带不确定模型参数和噪声方差的线性离散多传感器系统，基于极大极小鲁棒估值原理，该文提出一种鲁棒协方差交叉(CI)融合稳态Kalman滤波器。首先，用引入虚拟噪声补偿不确定模型参数，把模型参数和噪声方差两者不确定的多传感器系统转化为仅噪声方差不确定的系统。其次，应用Lyapunov方程证明局部鲁棒Kalman滤波器的鲁棒性，进而保证CI融合Kalman滤波的鲁棒性，且证明了CI融合器的鲁棒精度高于每个局部滤波器的鲁棒精度。最后，给出一个仿真例子来说明如何搜索不确定参数的鲁棒域，并验证所提出的鲁棒Kalman滤波器的优良性能。
- 多传感器信息融合 /
- 不确定系统 /
- 鲁棒Kalman滤波器 /
- 虚拟噪声 /
- 协方差交叉融合
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.
- Multisensor information fusion /
- Uuncertain system /
- Robust Kalman filter /
- Fictitious noise /
- Covariance Intersection (CI) fusion

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