Paper

Estimation of the Conditional State and Covariance With Taylor Polynomials

Volume Number:
16
Issue Number:
2
Pages:
126
–
142
Publication Date:
December 2021
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Abstract

A novel estimator is presented that expands the typical state and covariance update laws of Kalman filters to polynomial updates in the measurement. The filter employs Taylor series approximations of the nonlinear dynamic and measurement functions. All polynomials (functions approximation, state update, and covariance update) can be selected up to an arbitrary order to trade between filter’s accuracy and computational time. The performance of the algorithm is tested in numerical simulations.