SINS initial alignment using wavelet de-noising method for aircraft navigation

  • Muhammad Ilyas*
  • , Yunchun Yang
  • , Zhang Ren
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The error in the initial attitude determination is one of the most crucial sources of error for accurate inertial navigation. Estimation of the initial attitude angles accurately is the guarantee of precise determination of the position, velocity and attitude of a moving platform. For military aircrafts, the high precision and short time of initial alignment are key requirements. The required inertial signals (accelerometer specific force and gyro rates) are buried in high frequency measurement noise. When the aircraft engine is power on, the high frequency noise is introduced due to engine vibration and other disturbances. Kalman filter have been extensively used for initial fine alignment of Strapdown Inertial Navigation Systems (SINS). In this paper, Multi-resolution wavelet de-noising (MRWD) method is proposed to eliminate the high frequency noise from inertial sensor measurements before using for initial fine alignment. The proposed method is tested using real data sets collected when vehicle engine was power on. It has been observed that the accuracy of initial alignment can be improved through the proposed method.

Original languageEnglish
Title of host publicationWCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
Pages3921-3926
Number of pages6
DOIs
StatePublished - 2012
Event10th World Congress on Intelligent Control and Automation, WCICA 2012 - Beijing, China
Duration: 6 Jul 20128 Jul 2012

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)

Conference

Conference10th World Congress on Intelligent Control and Automation, WCICA 2012
Country/TerritoryChina
CityBeijing
Period6/07/128/07/12

Keywords

  • Initial Alignment
  • Kalman Filter
  • SINS
  • Wavelet De-noising

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