Sensor integration for satellite-based vehicular navigation using neural networks.
Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique.
Sharaf
Rashad
R
Noureldin
Aboelmagd
A
D000465:Algorithms; D001185:Artificial Intelligence; D040362:Geographic Information Systems; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D018531:Spacecraft / Q000295:instrumentation*; D018511:Systems Integration; D014159:Transducers