Weighted Fusion Robust Fractional Order Kalman Filter with Correlated Noise

The research results of Kalman filter can be applied in many fields such as weather forecasting, satellite positioning, aerospace and so on. The traditional Kalman filter generally considers mostly integer-order systems, while the Kalman filter estimation using fractional-order systems can obtain estimates closer to the real system, thus reducing the actual system error. In this paper, for the fractional-order system with uncertain noise variance and noise correlation, model transformation is carried out to transform the correlated noise of the fractional-order system into uncorrelated noise. Then a locally robust Kalman filter is proposed for this system, and the system with multiple sensors is weighted by matrix-weighted fusion method for weighted state fusion. The simulation results show that the algorithm is effective and feasible.