Abstract
The Kalman filter gain can be extracted from output signals but the covariance of the state error cannot be evaluated without knowledge of the covariance of the process and measurement noise Q and R. Among the methods that have been developed to estimate Q and R from measurements the two that have received most attention are based on linear relations between these matrices and: 1) the covariance function of the innovations from any stable filter or 2) the covariance function of the output measurements. This paper reviews the two approaches and offers some observations regarding how the initial estimate of the gain in the innovations approach may affect accuracy.
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Bulut, Y., Vines-Cavanaugh, D., Bernal, D. (2011). Process and Measurement Noise Estimation for Kalman Filtering. In: Proulx, T. (eds) Structural Dynamics, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9834-7_36
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DOI: https://doi.org/10.1007/978-1-4419-9834-7_36
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