GPS/INS integration utilizing dynamic neural networks for vehicular navigation

Authors:
Aboelmagd Noureldin;Ahmed El-Shafie;Mohamed Bayoumi
Affiliations:
NavINST - Navigation and Instrumentation Research Group, Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, Ontario, Canada K7K7B4 and Department of Ele ...;Department of Civil and Structure Engineering, University Kebangsaan Malaysia, Malaysia;Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada
Venue:
Information Fusion
Year:
2011

Citing 4
Cited 3

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

Engineering Applications of Artificial Intelligence
Asymptotic statistical theory of overtraining and cross-validation

IEEE Transactions on Neural Networks

A lane level positioning-based cooperative vehicle conflict resolution algorithm for unsignalized intersection collisions

Computers and Electrical Engineering
Optimization of Intelligent Approach for Low-Cost INS/GPS Navigation System

Journal of Intelligent and Robotic Systems
Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, methods based on Artificial Intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications integrating the Global Positioning System (GPS) with the Inertial Navigation System (INS). All existing AI-based methods are based on relating the INS error to the corresponding INS output at certain time instants and do not consider the dependence of the error on the past values of INS. This study, therefore, suggests the use of Input-Delayed Neural Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS position and velocity, respectively. This results in a more reliable positioning solution during long GPS outages. The proposed method is evaluated using road test data of different trajectories while both navigational and tactical grade INS are mounted inside land vehicles and integrated with GPS receivers. The performance of the IDNN - based model is also compared to both conventional (based mainly on Kalman filtering) and recently published AI - based techniques. The results showed significant improvement in positioning accuracy especially for cases of tactical grade INS and long GPS outages.