Inertial sensor measurement calibration using artificial intelligence techniques: a comparative study

Abstract Over the past decade, the Global Positioning System (GPS) has been extensively employed in land vehicle navigation systems. Inertial Navigation Systems (INSs) are utilized in scenarios where GPS fails to deliver consistent and reliable navigation solutions. It is well-known that the low-cost INS sensors can exhibit significant errors. To mitigate these errors, the measurements of position and velocity sensors from GPS are integrated and fused. In this study, an intelligent technique has been proposed to optimize the benefits of GPS and INS while minimizing their drawbacks. This study employed an artificial intelligence model to predict the instantaneous INS error based on instantaneous de-noised measurements and readings. Different artificial neural network (ANN) models have been tested, examined and compared. The results revealed that a considerable improvement has been reached in terms of ability to predict INS error during different GPS outages. However, a promising result have been found using RNN model. As compared to other ANN models, the RNN model gives better performance in terms of standard deviations of position and velocity errors. The standard deviations of prediction position errors resulting from RNN are equal to 0.0864, 0.0726, and 0.1297 min the sense of x, y and z channels, respectively, while the standard deviations of predicted velocity errors are 0.0025, 0.0029, and 0.0049 m s −1 on the x, y, and z axis, respectively.