Position tracking in urban environments using linear constraints and bias pseudo measurements

Abstract

In many GPS-Sensor based tracking applications, the obtained measurements suffer from time a correlated bias. This is due to shadowing and multipath scattering of the wireless GPS signals. In this paper, we applicate a Schmidt-Kalman Filter (SKF) in order to improve the tracking process of ground vehicles on roads. We investigate possibilities to integrate bias measurements obtained from road information into the position tracking filter. To this end, we assume a digital map is given which contains road information for the observed region. The estimated position by the sensor data is projected onto the road as a hard constraint. This enables us to detect and to eliminate regular sensor bias by extending the estimate state of the target by an estimate of the sensor bias.