Experimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios

Abstract

Abstract Current trends like Autonomous Driving (AD) increase the need for a precise, reliable, and continuous position at high velocities. In both natural and man-made environments, Global Navigation Satellite System (GNSS) signals suffer challenges such as multipath, attenuation, or loss-of-lock. As Highway Assist and Highway Pilot are AD next steps, multipath knowledge is necessary for this typical user-case and kinematic situations. This paper presents a multipath performance analysis for GPS and Galileo satellites in static, slow, and high kinematic scenarios. The data is provided from car test-drives in both controlled and unrestricted, near-natural environments. The Code-Minus-Carrier (CMC) and cycle-slip implementations are validated with measurement data from consecutive days. Multipath statistical models based on satellite elevation are evaluated for the three investigated scenarios. Static models derived from the car setup measurements for GPS L1, L2 and Galileo E1 and E5b show a good agreement with a state-of-the-art model as well as the enhanced Galileo signals performance. Slow kinematic multipath results in a controlled environment showed an improvement for both navigation systems compared to the static measurements at the same place. This result is confirmed by static and slow kinematic multipath simulations with the same GNSS receiver. Post-processing analysis on highway measurements revealed a bigger multipath bias, compared to the open-sky static and slow kinematic measurement campaigns. Although less critical as urban or rural, this indicates the presence of multipath in this kind of environment as well. The impact of different parameters, including receiver architecture and Signal-to-noise ratio (SNR) are analyzed and discussed. Differential position (DGNSS) based on code is computed for each epoch and compared against GNSS/INS integrated position for all three measurement campaigns. The most significant 3D absolute error occurs where the greatest multipath envelope is found.