In higher order Kalman filtering applications the analyst often has very little insight into the nature of the observability of the system. For example, there are situations where the filter may be estimating certain linear combinations of state variables quite well, but this is not apparent from a glance at the error covariance matrix. It is shown here that the eigenvalues and eigenvectors of the error covariance matrix, when properly normalized, can provide useful information about the observability of the system.
Observability, Eigenvalues, and Kalman Filtering
IEEE Transactions on Aerospace and Electronic Systems ; AES-19 , 2 ; 269-273
1983-03-01
1406907 byte
Article (Journal)
Electronic Resource
English
Observability and Kalman Filter
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