Abstract
The recurrence algorithms for the Cramer-Rao lower bound for a discrete-time nonlinear filtering problem in the conditions when a forcing noise, measurement errors and initial covariance matrix depend on the state vector to be estimated are derived. It is assumed that the state vector being estimated includes a subvector of time-invariant unknown parameters. Some examples are given to illustrate the applicability of the algorithms obtained.
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