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Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter

Bingbing Gao*, Gaoge Hu, Shesheng Gao, Yongmin Zhong, and Chengfan Gu
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.129-140, 2018

Abstract : "This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration."

Keyword : "NS/GNSS/CNS integration, linear minimum variance, multi-sensor data fusion, unscented Kalman filter."

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