Kalman filtering theory
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Robust Filtering via Semidefinite Programming with Applications to Target Tracking
SIAM Journal on Optimization
Minimax Estimation in Singular Uncertain Stochastic Models
Automation and Remote Control
Minimax-statistical approach to increasing reliability of measurement information processing
Automation and Remote Control
IEEE Transactions on Signal Processing
Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties
IEEE Transactions on Signal Processing
Brief Design and analysis of discrete-time robust Kalman filters
Automatica (Journal of IFAC)
Minimax estimation methods under ellipsoidal constraints
Automation and Remote Control
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A technique to construct the robust Kalman filter for process estimation in the difference linear stationary stochastic system with an unknown covariance observation error matrix was developed. Consideration was given to the algorithm of constructing the set of permissible covariance matrices from a priori statistical data. A numerical method for solution of the general minimax optimization problem was proposed; and on its basis an iterative algorithm to calculate the robust filter parameters was developed, and its convergence was proved. Results of the numerical experiment were presented.