Earticle

초록

영어

This article presents a new structure for solving global positioning system (GPS) outages for long periods without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS. Kalman filter (KF) is widely used in INS and GPS integration to present a forceful navigation solution by overcoming the GPS outage problems. However, KF is usually criticized for working under predefined models and for its observability problem of hidden state variables, sensor dependency, and linearization dependency. Therefore, this article proposes a dynamic adaptive neuro-fuzzy inference system (DANFIS) to predict the INS error during GPS outages based on the current and previous raw INS data. The proposed integrated system is evaluated using a real field test data. The performance of the proposed technique is also compared with the traditional artificial intelligence (AI) technique and KF. The results showed great improvements in positioning and especially in velocity for MEMS grade IMU and for different length of GPS outages.

목차

Abstract
 1. Introduction
 2. Dynamic Adaptive Neuro-fuzzy Inference System
  2.1 ANFIS Structure
  2.2 Adaptive Fuzzy System Training Algorithm [10, 22]
 3. Methodology
  3.1 DANFIS Architecture
 4. Results and Discussion
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Ahmed Mudheher Hasan [ Computer Systems Research Group, Department of Computer and Communication Systems Universiti Putra Malaysia ]
• Khairulmizam Samsudin [ Computer Systems Research Group, Department of Computer and Communication Systems Universiti Putra Malaysia ]
• Abdul Rahman Ramli [ Intelligent Systems and Robotics Laboratory Institute of Advanced Technology Universiti Putra Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605