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This paper addresses the design of robust measurement fusion Kalman filter for linear discrete-time multisensor systems with multiplicative noises perturbations both on state equation and measurement equations, and with uncertain noise variances. By introducing two fictitious noises, the system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle,...
In this paper, the design problems of robust weighted fusion Kalman estimators are addressed for multisensor time-varying system with uncertain linearly correlated noises. A unified design approach to obtain the robust Kalman estimators is presented where the robust Kalman filter and smoother are designed based on the robust Kalman predictor. The three robust weighted fusion time-varying Kalman estimators...
In this paper, the problem of designing robust steady-state Kalman filter is considered for linear discrete-time system with uncertain model parameters and noise variances. By the new approach of compensating the parameter uncertainties by a fictitious noise, the system model is converted into that with uncertain noise variances only. Using the minimax robust estimation principle, based on the worst-case...
For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when the model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying...
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