Issue link: https://insidegnss.epubxp.com/i/960969

44 Inside GNSS M A R C H / A P R I L 2 0 1 8 www.insidegnss.com e GPS and Galileo radio propagation delay, caused by the ions of the ionosphere, is estimated assuming the delay as a first order frequency-depended function, for details see Gehrt et alia, Additional Resources. erefore, this approach evaluates the received satellite signals and selects all satellites providing in dual-frequency mode. In some cases, some satellites do not provide two frequen- cies. For example, when older GPS satellites or the receiver is not able to receive more than one frequency of a particular satellite due to environmental circumstances. In this case, the well-known Klobuchar model for GPS measurements is used to correct ionospheric delays. Tropospheric delays for both systems are calculated by a common mapping function, first presented in a paper by Collins (Additional Resources). If available, differential corrections are preferred to increase the quality of the correction information. In this case, the cor- rection models for ionospheric and tropospheric delays are replaced. e correctional data is received from a network of reference stations. ey provide GPS observables and precise coordinates of a virtual base station close to the current user position. Currently this service is available for GPS only. ere- fore, this mode is currently applied during GPS pre-processing only and will be extended for Galileo in the near future. e correction approach reduces the expected error standard devia- tion in GPS pseudo-range observables below 1 meter. (Farrell and Mohinder et alia, Additional Resources). Dual-Constellation Extension Processing GPS and Galileo observables of the same receiver and within a single navigation filter requires a synchroniza- tion of both satellite system clocks. is synchronization was applied by introducing an extension in the GNSS pre-process- ing by Gehrt et alia at proceedings of the 2018 International Technical Meeting of e Institute of Navigation. Therefore, the EKF receives observables with the same clock source, which allows estimating one clock error and dri within the state estimation. As clock source, GPS or Galileo is defined as reference system. Furthermore, it is assumed, that the pseudo-range measurements of the secondary GNSS sys- tem contain an additional artificial signal transmission error Δs system , such that eq. (4) is extended as follows: Δs system can be interpreted as the product of the speed of light and the time difference Δt system between the reference system clock and the clock of the secondary system. In the case of GPS and Galileo, both clocks are real-time steering realiza- tions of UTC (Hahn and Powers, Additional Resources). erefore, it is assumed that Δt system is small and in turn Δs system can be neglected within the GNSS pre-processing step. is results in a simpler downstream architecture, where the GNSS pre-processing is first executed followed by the described syn- chronization step. Results In order to validate the enhancements of the conducted multi- constellation extensions and its online capability, extensive measurement campaigns are performed within realistic railway environments. In the experimental validation, the online-capa- ble navigation filter with three different settings with respect to the pseudo-range correction is evaluated and compared. e first setting corrects the ionospheric error of the pseudo-ranges of GPS L1 observables with the Klobuchar-Model. Setting two applies ionospheric corrections by means of dual-frequency approach in dual-constellation mode. ese settings utilize the tropospheric correction model. e third setting performs differential corrections, which takes both the ionospheric and the tropospheric errors into account. Zweigel et alia (Additional Resources) previously presented filter results in 2017. Herein, a best-case environment was chosen with little infrastructural obstacles and low system velocity. As expected, the navigation filter aided with GPS L1 observables and Klobuchar ionospheric corrections provides low accuracy due to the low quality ionospheric corrections. In comparison, the 2D precision of the dual-constellation mode with dual-frequency correction could be improved to a position error lower than 0.4 meters. is result is within the range of the high accuracy of GPS L1 with differential corrections. Additionally, extensive tests have been carried out in the railway test center Pruef- und Validationscenter Wegberg- Wildenrath (PCW) of the Siemens Company in Germany (Gehrt et alia, Additional Resources). Results confirm the comparable 2D accuracy of the dual-constellation mode com- pared to differential mode with an accuracy lower than 1 meter. erefore, track-resolved train localization using the presented navigation filter could be proved. Another test campaign has been performed, aiming on proving the high accuracy even during long duration tests in harsh railway environment. Figure 2 shows a general overview of the designated test track in Bavaria, Germany, which could be used thanks to the Chiemgauer Lokalbahn LEO from Bad Endorf to Obing. e test track is approximately 20 kilome- ters passing through urban environment, hills, and forest. e colored trajectory visualizes the whole track. e blue colored sub-track is the part which is evaluated here. Figure 3 gives an impression of the test campaign conditions with high conifers close to the single rail track. Due to the sea- son, the trees are covered with snow, causing signal reduction, R AILWAY APPLIC ATIONS FIGURE 2 Driven railway from Obing to Bad Endorf in Bavaria, Germany. Map Data: ©2018 GeoBasis/BKG(©2009), Google

- IGM_1.pdf
- IGM_2.pdf
- IGM_3.pdf
- IGM_4.pdf
- IGM_5.pdf
- IGM_6.pdf
- IGM_7.pdf
- IGM_8.pdf
- IGM_9.pdf
- IGM_10.pdf
- IGM_11.pdf
- IGM_12.pdf
- IGM_13.pdf
- IGM_14.pdf
- IGM_15.pdf
- IGM_16.pdf
- IGM_17.pdf
- IGM_18.pdf
- IGM_19.pdf
- IGM_20.pdf
- IGM_21.pdf
- IGM_22.pdf
- IGM_23.pdf
- IGM_24.pdf
- IGM_25.pdf
- IGM_26.pdf
- IGM_27.pdf
- IGM_28.pdf
- IGM_29.pdf
- IGM_30.pdf
- IGM_31.pdf
- IGM_32.pdf
- IGM_33.pdf
- IGM_34.pdf
- IGM_35.pdf
- IGM_36.pdf
- IGM_37.pdf
- IGM_38.pdf
- IGM_39.pdf
- IGM_40.pdf
- IGM_41.pdf
- IGM_42.pdf
- IGM_43.pdf
- IGM_44.pdf
- IGM_45.pdf
- IGM_46.pdf
- IGM_47.pdf
- IGM_48.pdf
- IGM_49.pdf
- IGM_50.pdf
- IGM_51.pdf
- IGM_52.pdf
- IGM_53.pdf
- IGM_54.pdf
- IGM_55.pdf
- IGM_56.pdf
- IGM_57.pdf
- IGM_58.pdf
- IGM_59.pdf
- IGM_60.pdf
- IGM_61.pdf
- IGM_62.pdf
- IGM_63.pdf
- IGM_64.pdf
- IGM_65.pdf
- IGM_66.pdf
- IGM_67.pdf
- IGM_68.pdf