A Comparative Hypothesis on Static and Kinematic Positioning Algorithms for CAT-I Aircraft Landings

Abstract

Global Positioning System (GPS) is affected by several factors such as measurement technique, environmental effects on the measurements, positioning solution, etc. Among these factors, identifying an optimal measurement technique is considered significant because an improper measurement technique provides a diverging solution. In this paper, one static positioning algorithm (i.e., Lease Square Estimator) and one kinematic positioning algorithm designated as Correntropy Extended Kalman Filter (CEKF) are proposed for precise GPS applications like Category-I (CAT-I) aircraft landings. The proposed algorithm uses correntropy criterion (CC) as an optimal criterion, a local similarity measure, unlike Minimum Mean Square Error (MMSE). Also, it uses an iterative approach called fixed point algorithm for renovating the rearward estimates. The simulation results show that the proposed algorithm outperforms the static positioning algorithm in 2-dimensional and 3-dimentsional surface.