On the Cramér-Rao lower bound under model mismatch

Fritsche, Carsten
Orguner, Umut
Özkan, Emre
Gustafsson, Fredrik
Cram´er-Rao lower bounds (CRLBs) are proposed for deterministic parameter estimation under model mismatch conditions where the assumed data model used in the design of the estimators differs from the true data model. The proposed CRLBs are defined for the family of estimators that may have a specified bias (gradient) with respect to the assumed model. The resulting CRLBs are calculated for a linear Gaussian measurement model and compared to the performance of the maximum likelihood estimator for the corresponding estimation problem.


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Fritsche, Carsten; Orguner, Umut; Gustafsson, Fredrik (2016-03-25)
Parametric Cramer-Rao lower bounds (CRLBs) are given for discrete-time systems with non-zero process noise. Recursive expressions for the conditional bias and mean-square-error (MSE) (given a specific state sequence) are obtained for Kalman filter estimating the states of a linear Gaussian system. It is discussed that Kalman filter is conditionally biased with a non-zero process noise realization in the given state sequence. Recursive parametric CRLBs are obtained for biased estimators for linear state esti...
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Citation Formats
C. Fritsche, U. Orguner, E. Özkan, and F. Gustafsson, “On the Cramér-Rao lower bound under model mismatch,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37264.