An Algorithm of Weighted Covariance for Centralized Asynchronous Fusion Based on Kalman

This paper proposed an algorithm of weighted covariance for centralized asynchronous fusion based on Kalman(WCCAF). The algorithm makes linear minimum mean square error (LMMSE) as the criterion, and considers the relevance of measurement noise. In each fusion cycle the steps are: at first, filter the new measurement and predict the state to fusion time using kalman filter. And then, fuse the new predict state with the existing estimated state of this moment using the method of weighting the estimated state error covariance. WCCAF algorithm has high information utilization, and compared with existing algorithms, it has better fusion precision. Recursively computing function of WCCAF makes it has very good calculated efficiency and strong real-time performance.