Mauro H. Riva, Jesús Díaz Díaz, Matthias Dagen, and Tobias Ortmaier
Noise covariance estimations, Kalman filter, Autocovariance method, Landweber iteration
Designing a Kalman filter requires knowledge about the stochastic part of the system. Thus, disturbances affecting states and measurements should be known. However, in practical application these disturbances are usually unknown. In this contribution a modification of the autocovariance least-square method is presented. This method converts the measurement and process noise covariance estimation problem into a least squares functional, which can be solved with a Landweber iteration to regularize the ill-posed problem. Then, a tuned Kalman filter gain can be calculated. A simulative evaluation is introduced to prove the method regarding robustness against modeling error and variance of the estimates.
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