Design and Development of GNSS Receiver for Challenging Environment

Abstract

The Global Navigation Satellite System (GNSS) is comprised of four major constellations, i.e. GPS, BeiDou, GLONASS and Galileo. GNSS technology is used to compute position, velocity and time (PVT) information of the receiver. The GNSS receiver chain has three main hardware modules: GNSS receive antenna, RF front end and baseband processor. This research work is focusing on baseband processing of the GNSS receiver. The receiver has the capability of acquiring GPS, BeiDou and GLONASS constellations. The baseband processing part is further divided into three software parts, i.e GNSS satellite acquisition, tracking and PVT computation. The position and velocity (PV) computation is the major processing part of the GNSS receiver and the output of the said part is mostly used by the user. This research work is also focued on the PV part of the GNSS receiver. The main problem with the GNSS PV solution is the precision in urban environments. The commercially available precision GNSS receivers are mostly using Extended Kalman Filter (EKF) to compute PV solution. The EKF is producing outliers in urban environment which is the major contributor of degraded PV solutions. This problem is resolved in this research work by using an Extended Kalman Filter along with least mean square (LMS) filter for position solution. The Kalman Filter requires high computation resources; for velocity computation, instead of Kalman Filter αβγ filter is applied on the doppler frequency of the satellite to smooth the velocity solution. The precision produced by αβγ filter is not as good as Kalman Filter but in velocity solution this filter achieved the desired precision. This filter is required less computation resources so in velocity solution this filter is preferred over Kalman Filter. In order to evaluate the above-mentioned technique simulation work was done on MATLAB and realization of the simulation work was done by exporting the code to embedded platform.