Interpretation of Statistical Errors for Precise GPS Receiver Positioning Using Recursive Least Squares and Extended Kalman Filter Approaches

Abstract

The Global Positioning system prevalently known as GPS is a range based positioning scheme that gives a 3D position of an obscure object on top of the earth. Object location accuracy commonly depends on the satellite clock error, atmospheric delays, multipath, poor satellite geometry, and receiver measurement noise, etc. Mostly, none of the above parameters do have constant behavior throughout the world and should be inspected territorially to give an exact solution. This paper mainly concentrates on the statistical analysis of the Recursive Least Squares (RLS) and Extended Kalman Filter (EKF) algorithms. Both of these algorithms were examined in this article by determining the most frequently used positional accuracy parameters in 2D and 3D spaces for statistical error analysis in a specific region, which expose their relationships and clarify several prevalent misinterpretations about precision. For the optimization of analytical outcomes, statistical attributes can be used. To evaluate these parameters, IISC, Bangalore, GPS receiver data were possessed with RLS and EKF methods.