Robust measurement fusion steady-state Kalman predictor for multisensor uncertain system

For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement fusion Kalman predictor is presented based on the worst-case conservative system with the conservative upper bounds of noise variances. The robustness and robust accuracy relation prove by Lyapunov equation approach. It is prove that it is equivalent to the robust centralized fusion Kalman predictor, and its robust accuracy is higher than that of each local robust Kalman predictor. A Monte-Carlo simulation example shows its correctness and effectiveness.