Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

2020 ◽  
Vol 34 (11) ◽  
pp. 1697-1725
Author(s):  
Chenjian Ran ◽  
Zili Deng
Keyword(s):  
Networked Systems ◽  
Multiplicative Noises ◽  
Random Measurement ◽  
Covariance Intersection ◽  
Integrated Covariance

Mode DependentH∞Filtering for Discrete-Time Networked Systems with Random Measurement Missing and Delays

2015 ◽  
Vol 33 (3) ◽  
pp. 519-534 ◽  
Author(s):  
Songlin Hu ◽  
Dong Yue ◽  
Xiangpeng Xie ◽  
Xiuxia Yin ◽  
Yunning Zhang
Keyword(s):  
Discrete Time ◽  
Networked Systems ◽  
Random Measurement

scholarly journals Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3059
Author(s):  
Christopher Funk ◽  
Benjamin Noack ◽  
Uwe D. Hanebeck
Keyword(s):  
Information Fusion ◽  
Data Transmission ◽  
Covariance Matrices ◽  
Networked Systems ◽  
High Dimensional ◽  
Available Bandwidth ◽  
Error Covariance ◽  
Covariance Intersection ◽  
Fusion Methods ◽  
Optimal Fusion

Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results. In this work, quantization methods for estimates and covariance matrices are presented and their usage with the optimal fusion formulas and covariance intersection is demonstrated. The proposed quantization methods significantly reduce the bandwidth required for data transmission while retaining unbiasedness and conservativeness of the considered fusion methods. Their performance is evaluated using simulations, showing their effectiveness even in the case of substantial data reduction.

Robust integrated covariance intersection fusion Kalman estimators for networked mixed uncertain time-varying systems

2020 ◽  
Author(s):  
Yuan Gao ◽  
Zili Deng
Keyword(s):  
Kalman Filtering ◽  
Estimation Error ◽  
Original System ◽  
Lyapunov Equation ◽  
Upper Bounds ◽  
Time Varying ◽  
Worst Case ◽  
Covariance Intersection ◽  
Integrated Covariance ◽  
White Noises

Abstract For the multisensor time-varying networked mixed uncertain systems with random one-step sensor delays and uncertain-variance multiplicative and linearly dependent additive white noises, a new augmented state method with fictitious noises is presented, by which the original system is transformed into a standard system without delays and with uncertain-variance fictitious white noises. According to the minimax robust estimation principle and the Kalman filtering theory, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the local and integrated covariance intersection (ICI) fused robust time-varying Kalman estimators (filter, predictor and smoother) are presented respectively in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness is proved by the extended Lyapunov equation method, and their accuracy relations are compared based on the traces of the variance matrices and the covariance ellipsoids, respectively. Specially, a universal ICI fusion robust Kalman filtering method of integrating the local robust estimators and their conservative cross-covariances is presented. It overcomes the drawbacks of the original covariance intersection (CI) fusion method and improves robust accuracy of the original CI fuser. A simulation example applied to two-mass spring system shows the effectiveness of the proposed methods and results.

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