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An Improved Adaptive Compensation H∞ Filtering Method for the SINS' Transfer Alignment Under a Complex Dynamic

Weiwei Lyu^1,2, Xianghong Cheng^3,4, Jinling Wang⁵

¹School of Instrument Science & Engineering, Southeast University, Nanjing 210096, China. lvww0220@seu.edu.cn.

Sensors (Basel, Switzerland)

|January 24, 2019

Summary

This study introduces an adaptive H∞ filter to enhance transfer alignment accuracy in strapdown inertial navigation systems (SINS). The method improves both rapid alignment and navigation precision in challenging dynamic conditions.

Keywords:

adaptive compensation complex dynamic environment filtering divergence robustness factor strapdown inertial navigation system (SINS)transfer alignment

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Area of Science:

Navigation Systems
Control Theory
Signal Processing

Background:

Transfer alignment in strapdown inertial navigation systems (SINS) faces significant challenges in complex dynamic environments.
Existing methods struggle with rapidity and accuracy, particularly concerning sensor error compensation and filtering divergence.

Purpose of the Study:

To improve the rapidity and accuracy of transfer alignment for SINS.
To develop an enhanced adaptive compensation H∞ filtering method to suppress filtering divergence and calibrate sensor errors.

Main Methods:

Application of velocity plus attitude matching in the transfer alignment model.
Establishment of an error compensation model for online inertial sensor calibration.
Proposal and optimization of an improved adaptive compensation H∞ filtering method with a dynamically adjustable robustness factor.

Main Results:

The proposed adaptive compensation H∞ filter effectively suppresses filtering divergence.
Aerial transfer alignment experiments demonstrate significant improvements in transfer alignment accuracy.
Enhanced pure inertial navigation accuracy was observed in complex dynamic environments.

Conclusions:

The adaptive compensation H∞ filtering method offers superior performance for transfer alignment in SINS.
The dynamic adjustment of the robustness factor enhances system accuracy and robustness.
The proposed method provides a viable solution for improving SINS performance under complex dynamic conditions.