THE ROLE OF KALMAN FILTER IN IMPROVING THE ACCURACY OF GPS KINEMATIC TECHNIQUE

This paper focuses on the estimation of the receiver coordinates (x, y) of a set of points based on pseudo range measurements of a single GPS receiver. The errors that affecting the GPS signal are degrading the accuracy of GPS position. Kalman filter is used to improve the accuracy of kinematic GPS point positioning using a single frequency I-COM GP 22 hand held receiver that obtained the coordinates along a part (30 km) of Cairo – Suez highway. A Kalman filter has the capability to characterize the noise sources in order to minimize their effect on the desired receiver output. The heart of the GPS-Kalman filter is an assumed model of how its state vector changes in time. The state vector contains the parameters describing the model and includes at least the receiver position (x, y). Kalman filter is a recursive estimator that produces the minimum covariance estimate of the state vector. Kalman filter sorts out information and weights the relative contributions of measurements compared with its assumed model. The proposed Kalman model was applied on the study area of Cairo –Suez highway. The results of proposed Kalman filter model give better accuracy with more consistency. Kalman filter technique is as an important tool for any dynamic process.