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

被引：65

作者：

Gao, Bingbing ^{[1
]}

Hu, Gaoge ^{[1
]}

Gao, Shesheng ^{[1
]}

Zhong, Yongmin ^{[2
]}

Gu, Chengfan ^{[2
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China

[2] RMIT Univ, Sch Engn, Bundoora, Vic 3083, Australia

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2018年 / 16卷 / 01期

基金：

中国国家自然科学基金;

关键词：

INS/GNSS/CNS integration; linear minimum variance; multi-sensor data fusion; unscented Kalman filter; RANDOM WEIGHTING ESTIMATION; PERFORMANCE ENHANCEMENT; INS/CNS INTEGRATION; NAVIGATION SYSTEM; ALGORITHM; MODEL; POSITION; UKF;

D O I：

10.1007/s12555-016-0801-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：129 / 140

页数：12

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