A Kalman Filter application for GNSS error correction in Intelligent Vehicles

Abstract

This proposes a Kalman Filter application for solving errors in the positioning of a moving vehicle in an everyday urban environment that is also cost effective. The GNSS sensor is a NOVATEL FlexPak-G2. It is installed in a car and takes measurements for several kilometres providing a wide range of data from which the position and the velocity are used to construct the model. The velocity is taken as a reference and three models are proposed in order to solve the inconsistencies of the GNSS sensor. The first model uses a Kalman Filter and is based on the hypothesis of a Rectilinear Uniform Motion. The second model uses a Kalman Filter and is based in the hypothesis of a Uniformly Accelerated Rectilinear Motion. The last of the three models uses an Unscented Kalman Filter and is based on the hypothesis of a Uniformly Accelerated Rectilinear Motion. At the end of the thesis, the three models will be compared and discussed in order to conclude the one that is best for the desired solution.