Nonlinear estimation using central difference information filter
Guoliang Liu, Florentin Wörgötter, Irene Markelić · 2011
In this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call central difference information filter (CDIF). As we know, the extended information filter (EIF) has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcomings can be compensated by utilizing sigma point information filters (SPIFs), e.g., the unscented information filter (UIF), which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirling's interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our algorithm, a classic space vehicle reentry tracking simulation is used.