Distributed State Fusion Using Sparse-Grid Quadrature Filter With Application to INS/CNS/GNSS Integration
Bingbing Gao, Gaoge Hu, Yongmin Zhong, Xinhe Zhu
Abstract
This paper presents a new methodology of distributed state fusion by using the sparse-grid quadrature filter to deal with the fusion estimation problem for multi-sensor nonlinear systems. In this methodology, the sparse-grid quadrature filter is performed in a distributed manner to process the information at each local node; and subsequently, a distributed state fusion approach based on matrix weighting is established in the sense of mean square error, in which a flexible procedure is developed based on the sparse-grid quadrature rule to calculate the cross-covariances between any two local estimators for multi-sensor nonlinear systems. The proposed methodology of distributed fusion can obtain higher fusion estimation accuracy in a flexible way, leading to improved fusion performance for multi-sensor nonlinear systems. The simulations and experiments in INS/CNS/GNSS (inertial navigation system/celestial navigation system/global navigation satellite system) integration verifies the effectiveness of the proposed methodology.