Comparison of nonlinear filtering techniques for inertial sensors error identification in INS/GPS integration

被引：1
|
作者：
Kaviani, S. [1 ]
Salarieh, H. [1 ]
Alasty, A. [1 ]
Abediny, M. [1 ]
机构：
[1] Sharif Univ Technol, Sch Mech Engn, POB 11365-9567, Tehran, Iran
来源：
SCIENTIA IRANICA | 2018年 / 25卷 / 03期
关键词：
Extended Kalman Filter (EKF); Unscented Kalman Filter (UKF); Extended Particle Filter (EPF); Unscented Particle Filter (UPF); GPS/INS INTEGRATION;
D O I：
10.24200/sci.2017.4328
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Nonlinear filtering techniques are used to fuse the Global Positioning System (GPS) with Inertial Navigation System (INS) to provide a robust and reliable navigation system with a performance superior to that of either INS or GPS alone. Prominent nonlinear estimators in this field are Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF, and PF based on EKF and UKF in an INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation, and bias/scale factor estimation are investigated using these four filtering algorithms. Both ground-vehicle experimental test and fiight simulation test have been utilized to evaluate the filters performance. (c) 2018 Sharif University of Technology. All rights reserved.
引用
收藏
页码：1281 / 1295
页数：15
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