Desensitized Filtering for Systems with Uncertain Parameters and Noise Correlation

This paper introduces estimation algorithms for systems with uncertain parameters and correlated noises. The algorithms are derived using the standard Kalman filter for correlated noises and the desensitized filtering technique for systems with uncertain parameters. A general algorithm and its special case are proposed. The latter updates statistics with explicit expressions, which makes it simpler and faster. The extended forms of the algorithms, which can be used for nonlinear systems, are also introduced. The developed algorithm is tested on an example, where the importance of the noise correlation information is shown.