Schmidt-Kalman Filters for Systems with Uncertain Parameters and Asynchronous Sampling

This paper introduces estimation algorithms for systems with uncertain parameters and asynchronous sampling. The algorithms are created by merging the Schmidt-Kalman filter (SKF) for systems with uncertain parameters and the conventional Kalman filter for systems with correlated noises. The system descriptions obtained by different discretization approaches are analyzed and used to develop the equivalent of the SKF. Then the SKF for systems with asynchronous sampling is developed by applying the SKF or its equivalent on the part of sampling period where the process and measurement noises are correlated. The accuracy of the novel filters is tested on a simple example.