Robust centralized and weighted measurement fusion Kalman predictors with multiplicative noises, uncertain noise variances, and missing measurements

DOI10.1007/s00034-017-0578-6zbMath1426.94054OpenAlexW2620525626MaRDI QIDQ2003222

Xue-Mei Wang, Wenqiang Liu, Deng, Zili

Publication date: 16 July 2019

Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00034-017-0578-6

zbMATH Keywords

missing measurementsmultiplicative noisesweighted measurement fusionuncertain noise variancesgeneralized fictitious noise techniqueminimax robust Kalman predictors

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Estimation and detection in stochastic control theory (93E10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (4)

Robust centralized and integrated covariance intersection fusion Kalman estimators for networked mixed‐uncertain systems ⋮ Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties ⋮ Distributed dimensionality reduction fusion Kalman filtering with quantized innovations ⋮ Robust fusion Kalman estimators for networked mixed uncertain systems with random one-step measurement delays, missing measurements, multiplicative noises and uncertain noise variances

Cites Work

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• Robust weighted fusion time-varying Kalman smoothers for multisensor system with uncertain noise variances
• Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal
• Optimal estimation of linear discrete-time systems with stochastic parameters
• Optimal and self-tuning weighted measurement fusion Wiener filter for the multisensor multichannel ARMA signals
• Kalman filtering with real-time applications.
• Design and analysis of discrete-time robust Kalman filters
• Robust centralized and weighted measurement fusion Kalman estimators for multisensor systems with multiplicative and uncertain-covariance linearly correlated white noises
• Robust filtering under stochastic parametric uncertainties
• Self-tuning decoupled information fusion Wiener state component filters and their convergence
• New approach to information fusion steady-state Kalman filtering
• A dilated LMI approach to robust performance analysis of linear time-invariant uncertain systems
• Optimal and self-tuning weighted measurement fusion Kalman filters and their asymptotic global optimality
• The guaranteed estimation performance filter for discrete-time descriptor systems with uncertain noise
• Optimal linear estimation fusion. I. Unified fusion rules
• Array algorithms for H/sup ∞/ estimation
• Optimal Estimation for Discrete-Time Linear Systems in the Presence of Multiplicative and Time-Correlated Additive Measurement Noises
• Robust Information Fusion Estimator for Multiple Delay-Tolerant Sensors With Different Failure Rates
• Trace bounds on the covariances of continuous-time systems with multiplicative noise
• Robust weighted fusion Kalman estimators for multisensor systems with multiplicative noises and uncertain‐covariances linearly correlated white noises
• Optimal recursive estimation with uncertain observation
• The optimality for the distributed Kalman filtering fusion with feedback

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