Cargando…

Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS

The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estima...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Dapeng, Guo, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712801/
https://www.ncbi.nlm.nih.gov/pubmed/29156576
http://dx.doi.org/10.3390/s17112670
Descripción
Sumario:The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H [Formula: see text] filter (SIH [Formula: see text] F) for improving both the accuracy and robustness of RTA. In this new nonlinear H [Formula: see text] filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H [Formula: see text] filter for the first time, and the resulting SIH [Formula: see text] F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH [Formula: see text] F and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H [Formula: see text] filter. Moreover, the SIH [Formula: see text] F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.