Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

被引：32

作者：

Qin, Honglei ^{[1
]}

Cong, Li ^{[1
]}

Sun, Xingli ^{[1
]}

机构：

[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

来源：

JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2012年 / 23卷 / 02期

基金：

中国国家自然科学基金;

关键词：

functional coefficient autoregressive (FAR); global positioning system (GPS); micro electromechanical system (MEMS); inertial navigation system (INS); self-constructive adaptive neuro-fuzzy inference system (SCANFIS); INS/GPS INTEGRATION; NEURAL-NETWORKS;

D O I：

10.1109/JSEE.2012.00033

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To improve the reliability and accuracy of the global positioning system (GPS)/micro electromechanical system (MEMS)-inertial navigation system (INS) integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive (FAR) modeling, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the traditional adaptive neuro-fuzzy inference system (ANFIS) method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS (SCANFIS) combined with the extended Kalman filter (EKF) is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the efficiency of the proposed methods.

引用

页码：256 / 264

页数：9

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