Summary: For the multisensor time-invariant system with uncertain parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, we can convert the uncertain system into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle and weighted least squares method, we present a robust weighted measurement fusion Kalman filter based on the worst-case conservative system with the conservative upper bounds of noise variances. We prove that this Kalman filter is equivalent to the robust centralized fusion Kalman filter, and its robust accuracy is higher than that of each local robust Kalman filter. A Monte-Carlo simulation example shows how to find the robust region of uncertain parameter and how to search the less-conservative upper bound of fictitious noise variances.