On Parametric model mismatch in nonlinear EKF Approximations
Document typeConference report
Rights accessOpen Access
European Commission's projectEngage - Knowledge Transfer Network proposed in response to the SESAR-ER3-01-2016 Call (EC-H2020-783287)
Standard state estimation techniques, either for linear or nonlinear systems, typically assume a perfect knowledge of the system, i.e., known process and measurement functions, and the corresponding noise statistics. The filter performance strongly depends on such knowledge and a possible model mismatch may lead to a significant performance degradation, but closed-form expressions for the resulting bias and covariance are not available. In this contribution, we explore the impact in extended Kalman filter approximations of a parametric model mismatch on both system functions and provide expressions for the estimator bias and covariance error induced by the mismatch. An illustrative example is shown to support the discussion.
CitationKhaledian, H. [et al.]. On Parametric model mismatch in nonlinear EKF Approximations. A: Asilomar Conference on Signals, Systems, and Computers. "Proceeding of 54th Asilomar Conference on Signals, Systems, and Computers". 2020,