Global Positioning System (GPS) is being used in aviation, nautical navigation and the orientation ashore. Further, it is used in land surveying and other applications where the determination of the exact position is required. Although GPS is known as a precise positioning system, there are several error sources which are categorized into three main groups including errors related to satellites, propagation and GPS receivers. In this paper we focus on the estimation of the receiver coordinates of a fixed point based on pseudorange measurements of a single GPS receiver. The estimation of the unknowns is achieved by introducing an Extended Kalman Filter (EKF) and Iterative Least Square (ILS) that processes. Kalman filter is the method most often used nowadays. It is based on some assumptions and if all the assumptions are met it can offer optimal estimation and prediction. The EKF not only works well in practice, but also it is theoretically attractive because it has been shown that it is the filter that minimizes the variance of the estimation mean square error.
Doma, M. I., & Sedeek, A. T. (2014). Estimation of GPS Position Using Iterative Least Squares and Extended Kalman Filter. ERJ. Engineering Research Journal, 37(1), 115-121. doi: 10.21608/erjm.2014.66880
MLA
M. I. Doma; A. T. Sedeek. "Estimation of GPS Position Using Iterative Least Squares and Extended Kalman Filter". ERJ. Engineering Research Journal, 37, 1, 2014, 115-121. doi: 10.21608/erjm.2014.66880
HARVARD
Doma, M. I., Sedeek, A. T. (2014). 'Estimation of GPS Position Using Iterative Least Squares and Extended Kalman Filter', ERJ. Engineering Research Journal, 37(1), pp. 115-121. doi: 10.21608/erjm.2014.66880
VANCOUVER
Doma, M. I., Sedeek, A. T. Estimation of GPS Position Using Iterative Least Squares and Extended Kalman Filter. ERJ. Engineering Research Journal, 2014; 37(1): 115-121. doi: 10.21608/erjm.2014.66880
)
View on SCiNiTO