Parameter Identification Method for SINS Initial Alignment under Inertial Frame

The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-positio...

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Bibliographic Details
Published in:Mathematical problems in engineering 2016-01, Vol.2016 (2016), p.1-9
Main Authors: Xue, Haijian, Zhou, Zhaofa, Guo, Xiaosong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alignment
Attitudes
Economic models
Frames
Identification methods
Inertial
Inertial navigation
Inertial reference systems
Mathematical models
Navigation systems
Noise
Normal distribution
Parameter identification
Real time
Self alignment
Strapdown inertial navigation
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Summary:The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-position method; then the time of initial alignment is greatly prolonged. For this issue, a novel self-alignment algorithm by parameter identification method under inertial frame for SINS is proposed in this paper. Firstly, this coarse alignment method using the gravity in the inertial frame as a reference is discussed to overcome the limit of dynamic disturbance on a rocking base and fulfill the requirement for the fine alignment. Secondly, the fine alignment method by parameter identification under inertial frame is formulated. The theoretical analysis results show that the fine alignment model is fully self-aligned with no external reference information and the gyrodrifts can be estimated in real time. The simulation results demonstrate that the proposed method can achieve rapid and highly accurate initial alignment for SINS.
ISSN:1024-123X
1563-5147
DOI:10.1155/2016/5301242