System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multisensor Data Fusion
Using covariance intersection for SLAM
Robotics and Autonomous Systems
Automatica (Journal of IFAC)
Brief paper: State estimation in the presence of bounded disturbances
Automatica (Journal of IFAC)
Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
An Interlaced Extended Kalman Filter for sensor networks localisation
International Journal of Sensor Networks
Target tracking in wireless sensor networks using compressed Kalman filter
International Journal of Sensor Networks
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
Robust discrete-time minimum-variance filtering
IEEE Transactions on Signal Processing
Finite-horizon robust Kalman filter design
IEEE Transactions on Signal Processing
A Minimax Chebyshev Estimator for Bounded Error Estimation
IEEE Transactions on Signal Processing
Brief Design and analysis of discrete-time robust Kalman filters
Automatica (Journal of IFAC)
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
Diffusion Kalman Filtering Based on Covariance Intersection
IEEE Transactions on Signal Processing
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This paper studies the problem of designing two-level robust sequential covariance intersection SCI fusion Kalman predictors for the clustering sensor networks with noise variances uncertainties. The sensor networks consist of many clusters, which are partitioned by the nearest neighbour rule. According to the minimax robust estimation principle, based on the worst-case conservative clustering sensor network with the conservative upper bound of noise variances, the two-level SCI fusion Kalman predictors are presented where the first level is the local SCI fusion predictors and the second level is the global SCI fusion predictor. This two-level fused structure can significantly reduce the communicational burden and save the energy sources. The robustness of the local and fused Kalman predictors is proved based on the Lyapunov equation method, and the robust accuracy relations are proved. A simulation example verifies the correctness and effectiveness of the proposed robust SCI predictor.