systems in Meteorology, Geophysics, Astronomy etc. For example, a Numerical Weather Prediction (NWP) system can now forecast observations with confidence intervals and their operational quality control can thus be improved. A sudden increase of uncertainty in predicting observations would indicate that important observations are missing (observability problem) or an unpredictable change of weather is taking place (controllability problem). Remote sensing and imaging from satellites are partly based on forecasted information. Controlling stability of the feedback between these forecasts and the satellite images requires a sensor fusion technique that is both fast and robust, which the FKF fulfills. The computational advantage of FKF is marginal for applications using only small amounts of data in real-time. Therefore, improved built-in calibration and data communication infrastructures need to be developed first and introduced to public use before personal gadgets and machine-to-machine devices can make the best out of FKF. References External links BBD - software FKF - formulas HWB - formulas The error covariance matrix of FKF - formulas There are other Fast Kalman Algorithms designed for special signal processing purposes, see e.g. Stabilizing the Fast Kalman Algorithms on IEEE Xplore Kalman filter recipes for real-time image processing Signal estimation Linear filters