MIMU/GPS information fusion: Normal cloud model based fuzzy adaptive filtering
Linlin Xia, Zhao Yao, MA Wen-jie, Su Chenxi, Huang Fadong · 2018
As a consequence of the statistical characteristics of noise changes with time or surrounding environment, typical MEMS IMUs/ GPS can hardly provide the ideal navigation accuracy as expected. A normal cloud model based fuzzy adaptive filtering (NCMFAF) is proposed for optimal state estimates, and the essential part of this derived filtering consists in its positive effect on updating the covariance difference degree between theoretical residuals and actual ones even with the large external variation of navigation process. Specifically, Normal cloud model module in cases is adopted as an adaptive controller, fulfilling the coefficient adjustment of measurement noise variance involved in typical Kalman filter, which, in turn, improves the performance of Kalman filter in dealing with the time/ environment -varying noise. With the numerical simulation being carried out, the results indicate the NCMFAF process can respond to this sudden statistical characteristics change of noise in real time, and the estimates accuracy of velocity/position is proved to be dramatically enhanced by reference to the dynamic environment statistics.