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Variance Identification in Kalman Filtering by Sean Shugar is a document available to read on EtoBox.
What is Variance Identification in Kalman Filtering about?
The document presents a technique for identifying the variances (covariances) of the process and measurement noise (Q and R matrices) for a Kalman filter when their true values are unknown. It begins by introducing the Kalman filtering problem and describing the assumptions made. It then shows that if the Kalman filter is not optimal, the autocorrelation of the innovation process can be used to obtain asymptotically unbiased and consistent estimates of Q and R, provided the form of Q is known and the numb
- Author
- Sean Shugar
- Language
- EN