Performance evaluation on GNSS, wheel speed sensor, yaw rate sensor, and gravity sensor integrated positioning algorithm for automotive navigation system

Abstract

The Global Navigation Satellite System (GNSS) positioning technique is widely used for the automotive navigation system since it can provide the stable and accurate position and velocity in the most road environments at an affordable price. However, the performance of GNSS positioning technique is degraded in certain areas, where GNSS signals are blocked by buildings and tunnel. To overcome this problem, the GNSS positioning technique should be integrated with dead reckoning (DR) sensors such as accelerometer, gyroscope, and odometer. Recently, the most passenger cars are equipped with the Advanced Driver Assistance System (ADAS) based on numerous sensors to improve safety and convenience in driving. Among sensors for the ADAS, vehicle dynamic sensors such as wheel speed sensor (WSS), yaw rate sensor (YRS), gravity sensor (GS) can be used for the DR algorithm since those sensors measure vehicle's motions. Therefore, this paper evaluates the vehicle positioning algorithm that integrate the GNSS with a three-dimensional dead reckoning based on WSS, YRS, and GS. The vehicle positioning algorithm is implemented through the extended Kalman filter of a loosely-coupled mode. Performance was evaluated through tests carried out in real driving trajectory including various GNSS signals reception environments. It is found that the proposed algorithm can be an alternative solution to compensate the limitation of the GNSS positioning technique, without the use of a low-cost inertial measurement unit. © The Authors, published by EDP Sciences, 2019.