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 depen- dence of the error on the past values of INS. This study, therefore, suggests the use of Input-Delayed Neu- ral 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 dur- ing 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.
Most of today’s land vehicles are equipped with Global Position- ing Systems (GPS) to provide accurate position and velocity infor- mation.However, there are several situations where GPS experience either total system outage (due to satellite signal block- age) or deterioration of accuracy (due to multipath effects and clock bias error). Therefore, GPS is usually combined with Inertial Navigation System (INS), which is a self-contained system incorpo- rating three orthogonal accelerometers and three orthogonal gyro- scopes. These monitor the vehicle’s linear accelerations and rotation rates. A set of mathematical transformations and integra- tions with respect to time are applied to these raw measurements to determine position, velocity and attitude information. However, the INS accuracy deteriorates with time due to possible inherent sensor errors (white noise, correlated random noise, bias instabil- ity, and angle random walk) that exhibit considerable long-term growth .
The integration of GPS and INS, therefore, provides a navigation system that has superior performance in comparison with either aGPS or an INS stand-alone system. For instance, GPS position com- ponents have approximately white noise characteristics with bounded errors and can therefore be used to update INS and im- prove its long-term accuracy. On the other hand, INS provides posi- tioning information during GPS outages thus assisting GPS signal reacquisition after an outage and reducing the search domain re- quired for detecting and correcting GPS cycle slips. INS is also capa- ble of providing positioning and attitude information at higher data rates than GPS.
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