Abstract
Global Positioning System (GPS) is a common choice for positioning in Land vehicle navigation system technology. However, GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation System (INS) is the implementation of inertial sensors to determine the position and orientation of a vehicle. As such, inertial navigation has unbounded error growth since the error accumulates at each step. Thus in order to contain these errors some form of external aiding is required. The availability of low cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop INS using an inertial measurement unit (IMU). INS/GPS integrated systems, based on MEMS technology, are recently subject of great interest. Typically IMU's are very expensive systems; however this INS will use ldquolow costrdquo components. The current performance achieved by low-cost IMUs is still relatively poor due to the large inertial sensor errors. This can significantly affect the performance of the integrated system in situations of low satellite visibility. Usually, GPS and INS are integrated with a loosely coupled scheme, which is suitable for those applications where satellite availability is always good. The integration of GPS with INS can be implemented using a Kalman filter. For simplicity and in case of land vehicle navigation in short travelling distance the earth can be considered as flat earth model. This paper introduces a low cost INS/GPS algorithm that can be used for navigation of a car like robot. The data fusion process is done with an extended Kalman filter in cascade configuration mode. In order to perform numerical simulations, MATLAB software has been developed. Loosely coupled configuration is considered. The results obtained in this work demonstrate that a low-cost INS/GPS navigation system is partially capable of meeting the performance requirements f...