Comparative Analysis of Least Squares Method and Extended Kalman Filter for Position Estimation in GPS Receiver

GPS is a system of obtaining the position of any object on or above the earth surface. Global Positioning System (GPS) has been incorporated into many devices in everyday life. However, GPS receiver design is challenging depending on the user’s operating environment. The accuracy of GPS position estimate is affected by various factors like ionospheric delay, tropospheric delay, various multi-path effects, and number of satellites in view and navigational solution employed. Multipath propagation to low signal strength are examples of some of these challenges. This paper provides a comparative analysis of position estimation techniques in a GPS receiver. These techniques are the Least-Squares (LS) method and Extended Kalman Filter Method (EKF). In this, the data possessed with a dual-frequency GPS receiver is placed at the reference point (X-1687535, Y-5809975, Z-2014102). For this reference point iterative assessments of satellite transmission moment, receiver time, and position are carried out to determine instantaneous estimates of the receiver location. The work explains the design and implementation of a software-defined GPS receiver in real-time. We use five satellites to estimate the position of the receiver. The performance evaluation of position estimation accuracy over the region is carried out based on position coordinates, root mean square error (RMSE), and standard deviation. The experimental evaluation demonstrates that the Extended Kalman Filter provides a more precise and stable estimate than LS method.