Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS

被引：65

作者：

Cui, Bingbo ^{[1
,2
]}

Chen, Xiyuan ^{[1
,2
]}

Xu, Yuan ^{[3
]}

Huang, Haoqian ^{[1
,2
]}

Liu, Xiao ^{[1
,2
]}

机构：

[1] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China

[2] Minist Educ, Key Lab Microinertial Instrument & Adv Nav Techno, Nanjing 210096, Jiangsu, Peoples R China

[3] Univ Jinan, Sch Elect Engn, Jinan 250022, Shandong, Peoples R China

来源：

ISA TRANSACTIONS | 2017年 / 66卷

基金：

中国国家自然科学基金;

关键词：

Integrate navigation; Iterated Gaussian filter; Cubature Kalman filter; Adaptive filter; NAVIGATION SYSTEM; GPS; TRACKING;

D O I：

10.1016/j.isatra.2016.09.010

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：460 / 468

页数：9

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