Document Type : Research Paper


1 electromechanical engineering, university of technology, Baghdad,iraq

2 Department of Electromechanical Engineering, University of Technology, Baghdad, Iraq

3 Department Mechanical Engineering, Al-Salam university, Baghdad, Iraq


Global Positioning System (GPS) has become important and necessary in daily life.  It is possible to reach any destination using GPS, which is included in many lands and marine applications.  In this work, GPS was applied to a real navigation boat, integrated with the inertial navigation system (INS) device, and installed on the boat. The navigational devices were linked to the (mission planer) program, through which the results of the navigation process were shown. The system can provide better navigation performance accuracy and reliability due to the integration between GPS and INS. The data extracted from the navigation devices are processed using the Gaussian process (GP) prediction algorithm, to perform the GPS synchronization with the INS and predict the GPS cut-off values for specified periods. The prediction results of the GP algorithm are effective for the cut-off GPS data as the apparent error amount of the algorithm is low. In addition the inertial navigation system provides the location, speed, and position of the boat, but it contains a cumulative error that increases over time. On the other hand, the GPS better accuracy with a lower data rate than  the INS, so the integration system between INS/GPS is necessary. It must be developed to overcome the negatives in both systems. Two types of integration were introduced and implemented herein: loosely and tightly. From the results obtained, one can see that the tight system is better at improving errors.

Graphical Abstract


  • The GPS data values were predicted when a signal was cut off for any reason using the Gaussian Process algorithm.
  • Synchronization between GPS values and INS values for integration was employed.
  • Implementation of the work was done by the program MATLAB.
  • Tthe integration was carried out using the Extended Kalman filter.


Main Subjects

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