SISE Prediction and Monitoring Concept

The SISE extrapolation interval is composed on two contributions:

  • a short processing delay needed to obtain SISE estimates from the set of data
  • a validity interval in which the confidence of SISE prediction is high
  • The recursively of the process guarantees to the user a continuous availability of updated SISE estimates.

The SISE will be estimated in two manners:

  • the first is based on data from a local network of stations. It consists of cleaning the observation from the above estimated errors, and estimating several coefficients of a specific model using these cleaned observation residuals.
  • The second is based on ultrarapid predictions from IGS: the broadcast position/clock of GNSS satellites is compared with IGS predictions and the difference is projected to the line of sight to the station.
  • The two methods are complementary: the first is more relayed to a real time service provision, while the second is more global. The motivation to consider both is to merge the information, and to provide methods for comparison and validation of the results.

Local Ionospheric Correction Concept

The ionospheric correction will be computed locally by the reference station using dual (for GPS/GLONASS) or three (for Galileo) frequency techniques.

This error and a short term prediction will be available to a local user in a neighborhood (the size to be determined within the research activity) of the reference station.

We believe this approach to be feasible because the ionospheric effects on ray tracing tend to change slowly with time and to maintain a high spatial correlation on the scale of tens of kilometers.

Local Tropospheric Correction Concept

The tropospheric correction includes a term based on Near Real Time data delivered already now by several networks, such as the COST 716 project of the EU, or EUREF.

The tropospheric correction term will be computed at the station on the basis of local pressure and temperature data measured by traditional sensors, and then extrapolated to a nearby user.

The prediction is, in this case, more problematic than for the ionospheric effect due to the higher time variability and the shorter correlation scale of the tropospheric terms.

The refresh rate of this info needs to be understood as well as its decay in accuracy, as a function of the distance from the permanent station and the epoch since it was issued.

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