Nonlinear Deterministic Filter for Inertial Navigation and Bias Estimation with Guaranteed Performance

被引：1

作者：

Ludher, Ajay Singh ^{[1
]}

Tawhid, Marium ^{[1
]}

Hashim, Hashim A. ^{[1
]}

机构：

[1] Thompson Rivers Univ, Dept Engn & Appl Sci, Software Engn, Kamloops, BC V2C 0C8, Canada

来源：

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) | 2021年

关键词：

EXTENDED KALMAN FILTER; LOCALIZATION; ATTITUDE;

D O I：

10.1109/ITSC48978.2021.9565015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF). It has been known that the true navigation dynamics are highly nonlinear modeled on the Lie Group of SE2(3). In this paper, a nonlinear filter for inertial navigation is proposed. The filter ensures systematic convergence of the error components starting from almost any initial condition. Also, the errors converge asymptotically to the origin. Experimental results validates the robustness of the proposed filter.

引用

页码：3405 / 3411

页数：7

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