Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system

A. El-Shafie, A. Najah, Othman A. Karim

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Inertial navigation system (INS) relying on gyroscopes and accelerometers has been recently utilized in land vehicles. These INS sensors are integrated with Global Positioning System (GPS) to provide reliable positioning solutions in case of GPS outages that commonly occur in urban canyons. The major inadequacies of INS navigation sensors are the high noise level and the large bias instabilities that are stochastic in nature. The effects of these inadequacies manifest themselves as large position errors during GPS outages. Wavelet analysis is a signal processing method which is recently auspicious by many researchers due to its advantageous adaptation to non-stationary signals and able to perform analysis in both time and frequency domain over other signal processing methods such as the fast Fourier transform in some fields. This research proposes the utilization of wavelet de-nosing to improve the signal-to-noise ratio of each of the INS sensors. In addition, a neuro-fuzzy module is used to provide a reliable prediction of the vehicle position during GPS outages. The results from a road test experiment show the effectiveness of the proposed wavelet-neuro-fuzzy module.

Original languageEnglish
Pages (from-to)1905-1916
Number of pages12
JournalNeural Computing and Applications
Volume24
Issue number7-8
DOIs
Publication statusPublished - 2014

Fingerprint

Inertial navigation systems
Navigation systems
Global positioning system
Outages
Sensors
Signal processing
Wavelet analysis
Gyroscopes
Accelerometers
Fast Fourier transforms
Signal to noise ratio
Navigation
Experiments

Keywords

  • GPS/INS
  • Navigation system
  • Wavelet-ANFIS

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system. / El-Shafie, A.; Najah, A.; A. Karim, Othman.

In: Neural Computing and Applications, Vol. 24, No. 7-8, 2014, p. 1905-1916.

Research output: Contribution to journalArticle

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