Rectification of GNSS-based collaborative positioning using 3D building models in urban areas
- 169 Downloads
GNSS collaborative positioning receives great attention because of the rapid development of vehicle-to-vehicle communication. Its current bottleneck is in urban areas. During the relative positioning using GNSS double-difference pseudorange measurements, the multipath effects and non-line-of-sight (NLOS) reception cannot be eliminated, or even worse, both might be aggregated. It has been widely demonstrated that 3D map aided GNSS can mitigate or even correct the multipath and NLOS effects. We, therefore, investigate the potential of aiding GNSS collaborative positioning using 3D city models. These models are used in two phases. First, the building models are used to exclude NLOS measurements at a single receiver using GNSS shadow matching positioning. Second, the models are used together with broadcast ephemeris data to generate a predicted GNSS positioning error map. Based on this error map, each receiver will be identified as experiencing healthy or degraded conditions. The receiver experiencing degraded condition will be improved by the receiver experiencing the healthy condition, hence the aspect of collaborative positioning. Five low-cost GNSS receivers are used to conduct experiments. According to the result, the positioning accuracy of the receiver in a deep urban area improves from 46.2 to 14.4 m.
KeywordsCollaborative positioning 3D building models Urban canyon Consistency check NLOS
The authors acknowledge the support of the Hong Kong PolyU startup fund on the project 1-ZVKZ, “Navigation for Autonomous Driving Vehicle using Sensor Integration”.
- Angrisano A, Gaglione S, Gioia C (2012) RAIM algorithms for aided GNSS in urban scenario. In: ubiquitous positioning, indoor navigation, and location based service (UPINLBS), IEEE. Helsinki, October 4, pp 1–9Google Scholar
- Blanch J, Walter T, Enge P (2015) Fast multiple fault exclusion with a large number of measurements. In Proceedings of the ION ITM 2015, Institute of Navigation. Dana Point, California, USA, January 26–28, pp 696–701Google Scholar
- Elazab M, Noureldin A, Hassanein HS (2016) Integrated cooperative localization for Vehicular networks with partial GPS access in urban canyons. Veh Commun 9:242–253Google Scholar
- Groves PD (2013) Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, NorwoodGoogle Scholar
- Hsu LT (2017) GNSS multipath detection using a machine learning approach. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), October 16–19, pp 1–6. https://doi.org/10.1109/itsc.2017.8317700
- Levinson J, Montemerlo M, Thrun S (2007) Map-based precision vehicle localization in urban environments. In Robotics: science and systemsGoogle Scholar
- Ng HF, Zhang G, Hsu L-T (2019) Range-based 3D mapping aided GNSS with NLOS correction based on skyplot with building boundaries. In Proceedings of the ION Pacific PNT 2019, Institute of Navigation. Honolulu, Hawaii, USA, April 8–11, pp 737–751Google Scholar
- Tiberius C, Verbree E (2004) GNSS positioning accuracy and availability within location based services: the advantages of combined GPS-Galileo positioning. In: Granados GS (ed) 2nd ESA/Estec workshop on satellite navigation user equipment technologies. ESA Publications Division, Noordwijk, pp 1–12Google Scholar
- Zhang G, Wen W, Hsu LT (2018) A novel GNSS based V2V cooperative localization to exclude multipath effect using consistency checks. In Proceedings of the IEEE/ION PLANS 2018, Institute of Navigation. Monterey, California, USA, April 23–26, pp 1465–1472. https://doi.org/10.1109/plans.2018.8373540
- Ziedan NI (2017) Urban positioning accuracy enhancement utilizing 3D buildings model and accelerated ray tracing algorithm. In Proceedings of the ION GNSS 2017, Institute of Navigation. Portland, Oregon, USA, September 25–29, pp 3253–3268Google Scholar