Particle Filter Based Non-Line-of-Sight Mitigation for Precise GNSS Vehicle Localization

Global Satellite Navigation Systems (GNSS) based localization in the context of Advanced Driver Assistance Systems and Intelligent Transportation Systems often requires not only accuracy but focuses on integrity as well. Especially, for safety relevant tasks the computation of proper confidence levels even at degraded environments is of major importance. Low cost solutions that integrate GNSS and additional in-vehicle sensor information are able to bridge short periods of time with limited GNSS accessibility and can therefore improve availability and accuracy. However, non-line-of-sight (NLOS) effects in urban areas need special attention. This error source violates the estimated confidence and introduces an unobservable bias to the position solution. The algorithmic detection of these effects and the proper propagation of all uncertainties within a Bayes framework is one of the key technologies towards the adoption of GNSS for safety critical applications. This paper proposes a probabilistic NLOS detection algorithm that is able to improve both - accuracy and integrity of the position estimate in urban areas. As an extension of a previous implementation by the authors based on an unscented Kalman Filter the proposed system is implemented as a particle filter in order to meet automotive requirements in terms of real time and scalability.