Permanent train-side GNSS multipath characterization considering time-correlation for safe railway localization

Abstract

Railway transportation systems require accurate and robust localization for safe operations. In the future, Railway signaling is expected to rely on onboard sensors like Global Navigation Satellite Systems (GNSS) in order to reduce installation and maintenance. GNSS positioning can be however challenging in railway environments. In particular, multipath is one of the largest local errors due to its fast dynamic and complex nature.

This paper proposes to model code multipath by distinguishing between error contribution caused by the antenna installation on the train roof and by the reflections of surrounding objects like e.g., buildings during train operation. Multipath caused by the antenna installation is expected to have similar stochastic properties for a given satellite elevation with respect to the user, while multipath caused by the environment is expected to be variable during the operation. In this paper we focus on the derivation of a conservative error model of multipath and noise caused by the antenna installation and the vehicle structure surrounding the antenna. To do this, we first isolate multipath and noise from other GNSS errors using the Code-Minus-Carrier method with data collected in open sky scenarios. Second, an overbounding error model is derived. The limitation of modeling with restricted set of real data typically found in practice is discussed in detail and we review methods that ensure the independency of samples to build the final probability distributions. A new approach to create separate data sets is ultimately proposed to derive an overbounding sigma (see Figure 1).

The derived models can be used as a reference nominal error models to build the null hypothesis of fault detection algorithms that detects the presence of excessive multipath in dynamic scenarios. Results are obtained for different train installations with real data collected during the EU ERSAT-GCC and RAILGAP projects.