The demand of vehicle navigation and guidance has been urgent for many years. The idea of integrating multisensor navigation systems was implemented. The most eíĩicient multisensor coníiguration is thc systcm integrating an inertial navigation system (INS) consisting OÍMEMS based micro sensors and a global positioning system (GPS). In such system, the GPS is used for providing position and vclocity whereas the INS for providing orientation. The estimation of the system errors is performed by a Kalman filter (KF). A serious problem occurs in the INS/GPS systcm application that is caused by the accidental GPS signal blockages. In this paper, thc main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using điíĩerent methods. The overall pcríbrmance of the system have been analyzcd by expcrimentation data; and results show that thcse mcthods indced improve the quality of the navigation and guidance systems.
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The demand of vehicle navigation and guidance has been urgent for many years. The idea of integrating multisensor navigation systems was implemented. The most eíĩicient multisensor coníiguration is thc systcm integrating an inertial navigation system (INS) consisting OÍMEMS based micro sensors and a global positioning system (GPS). In such system, the GPS is used for providing position and vclocity whereas the INS for providing orientation. The estimation of the system errors is performed by a Kalman filter (KF). A serious problem occurs in the INS/GPS systcm application that is caused by the accidental GPS signal blockages. In this paper, thc main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using điíĩerent methods. The overall pcríbrmance of the system have been analyzcd by expcrimentation data; and results show that thcse mcthods indced improve the quality of the navigation and guidance systems.
