Nonlinear GPS models for position estimate using low-cost GPS receiver

This paper presents the results obtained in our research about application of advanced signal processing to GPS based position estimation. This technique can be used in low-cost standalone GPS receiver with robust and high accuracy. The motivation of this research is to address the GPS positioning problems in vehicle navigation under bad circumstances where the visible satellites are visible satellites are variable frequently or less than four. A new model for position and velocity estimation are developed for nonlinear filtering. The model is nonlinear and has variable measurement number for coping with an arbitrary number of satellites. The model is investigated applying it to unscented Kalman filter for position estimation. The first experimental results that comprise the comparison of estimation results obtained with different algorithms are then presented. Comparison shows that our approaches provide better estimation than other solutions.