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

Wang Xue-mei; Liu Wen-qiang; Deng Zi-li

doi:10.11999/JEIT141515

Volume 37 Issue 8

Aug. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(8): 1900-1905

Wang Xue-mei, Liu Wen-qiang, Deng Zi-li. Robust Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1900-1905. doi: 10.11999/JEIT141515

Citation:

Wang Xue-mei, Liu Wen-qiang, Deng Zi-li. Robust Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1900-1905. doi: 10.11999/JEIT141515

Citation:

PDF( 823 KB)

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

doi: 10.11999/JEIT141515

1.
2.
(Electronic Engineering College, Heilongjiang University, Harbin 150080, China)

Received Date: 2014-11-27
Rev Recd Date: 2015-03-27
Publish Date: 2015-08-19

Abstract

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

FullText(HTML)

References(24)

References

Hall D L and Llinas J. An introduction to multisensor data fusion[J]. Proceedings of the IEEE, 1997, 85(1): 6-23.

Julier S J and Uhlmann J K. General Decentralized Data Fusion with Covariance Intersection. Handbook of Multisensor Data Fusion: Theory and Practice[M]. Second Edition, New York: CRC Press, 2008: 319-342.

Hajiyev C G and Soken H E. Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/ actuator faults[J]. AerospaceScience and Technology, 2013, 28(1): 376-383.

Le M S, Shin H S, Markham K, et al..?Cooperative allocation and guidance for air defence application[J]. Control Engineering Practice, 2014, 32:?236-244.

Feng J X, Wang Z D, and Zeng M. Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises[J]. Information Fusion, 2013, 14(1): 78-86.

Li X R, Zhu Y M, and Han C Z. Optimal linear estimation fusion-Part I: Unified fusion rules[C]. IEEE Transations on Information Theory, 2003, 49(9): 2192-2208.

Julier S J and Uhlmann J K. Non-divergent estimation algorithm in the presence of unknown correlations[C]. Proceedings of the IEEE American Control Conference, Albuquerque, 1997: 2369-2373.

Uhlmann J K. Covariance consistency methods for fault-tolerant distributed data fusion[J]. Information Fusion, 2003, 4(3): 201-215.

Julier S J and Uhlmann J K. Using covariance intersection for SLAM[J]. Robotics and Autonomous Systems, 2007, 55(1): 3-20.

Sijs J and Lazar M. State fusion with unknown correlation: Ellipsoidal intersection[J]. Automatica, 2012, 48: 1874-1878.

Lazarus S B, Tsourdos A, Zbikowski R, et al.. Robust localisation using data fusion via integration of covariance intersection and interval analysis[C]. International Conference on Control, Automation and Systems COEX, Seoul, Korea, 2007: 199-206.

Ferreira J and Waldmann J. Covariance intersection-based sensor fusion for sounding rocket tracking and impact area prediction[J]. Control Engineering Practice, 2007, 15(4): 389-409.

Qi W J, Zhang P, and Deng Z L. Robust sequential covariance intersection fusion kalman filtering over multi-agent sensor networks with measurement delays and uncertain noise variances[J]. Acta Automatica Sinica, 2014, 40(11): 2632-2642.

Gao Q, Chen S Y, Leung H R, et al.. Covariance intersection based image fusion technique with application to pansharpening in remote sensing[J]. Information Sciences, 2010, 180(18): 3434-3443.

Deng Z L, Zhang P, Qi W J, et al.. Sequential covariance intersection fusion Kalman filter[J]. Information Sciences, 2012, 189: 293309.

Sriyananda H. A simple method for the control of divergence in Kalman filter algorithms[J]. International Journal of Control, 1972, 16(6): 1101-1106.

Lewis F L, Xie L H, and Popa D. Optimal and Robust Estimation[M]. Second Edition, New York: CRC Press, 2007: 315-340.

Qu X M and Zhou J. The optimal robust finite-horizon Kalman filtering for multiple sensors with different stochastic failure rates[J]. Applied Mathematics Letters, 2013, 26(1): 80-86.

Deng Z L, Zhang P, Qi W J, et al.. The accuracy comparison of multisensor covariance intersection fuser and three weighting fusers[J]. Information Fusion, 2013, 14(2): 177-185.

Qi W J, Zhang P, and Deng Z L. Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances[J]. Signal Processing, 2014(99): 185-200.

Qi W J, Zhang P, Nie G H, et al.. Robust weighted fusion Kalman predictors with uncertain noise variances[J]. Digital Signal Processing, 2014(30): 37-54.

Qi W J, Zhang P, and Deng Z L. Robust weighted fusion time-varying Kalman smoothers for multisensory system with uncertain noise variances[J]. Information Sciences, 2014 (282): 15-37.

Qu X M. A mini-max fusion strategy in distributedmulti- sensor system[C]. International Conference on System Science and Engineering, Xiamen, China, 2012: 330-333.

Kailath T, Sayed A H, and Hassibi B. Linear Estimation[M]. New York: Prentice Hall, 2000, 766-772.

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(7)

1.	赵书红，董绍武，白杉杉，高喆. 一种优化的频率驾驭算法研究. 电子与信息学报. 2021(05): 1457-1464 . 本站查看
2.	陶贵丽，刘文强，张兴华，牛晓霞. 带丢包不确定广义系统鲁棒Kalman预报器. 系统科学与数学. 2021(05): 1215-1232 .
3.	谢卫，王前东. 一种基于自适应网格剖分的协方差交集融合新算法. 电讯技术. 2019(09): 1067-1074 .
4.	刘振亚，高敏，程呈. 基于理想弹道鲁棒容积卡尔曼滤波视线角估计. 系统工程与电子技术. 2018(02): 409-416 .
5.	秦文利，胡捍英，陈松. 基于带势概率假设密度粒子滤波的MIMO雷达检测前跟踪算法. 信息工程大学学报. 2018(02): 140-145 .
6.	王雪梅，刘文强，邓自立. 带丢失观测和不确定噪声方差系统改进的鲁棒协方差交叉融合稳态Kalman滤波器. 控制理论与应用. 2016(07): 973-979 .
7.	王雪梅，刘文强，邓自立. 带不确定协方差线性相关白噪声系统改进的鲁棒协方差交叉融合稳态Kalman估值器. 控制与决策. 2016(10): 1749-1756 .