A general automotive navigation system is a satellite navigation system designed for use in automobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and light weight micro-machined electromechanical systems (MEMS) inertial sensors have made the application ofinertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation.

In location tracking system, sensors are used either individually or in conjunction like in data fusion.However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate.The task of this thesis work was to take data from two sensors, and use an estimation technique to provide an accurate estimate of the true location. The proposed sensors were an accelerometer and aGPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter.

This thesis report presents an insight to both the proposed sensors and different estimation techniques.Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.