Modeling

• Modeling of tides, subdaily Earth rotation, and nutation are conforming with IERS Conventions 2010
• Interpolation of ocean tidal loading to 342 constituents (HARDISP) according to IERS2010 conventions
• S1/S2 atmosphere pressure loading tidal corrections
• Center of mass corrections for ocean and atmospheric tidal loading
• Use of various gravity fields easily implementable
• DE405 ephemeris series for planets as well as the gravitational effect of ocean and solid Earth tides in the orbit integrator (ORBGEN)
• Piece-wise linear troposphere parameter representation, with possibility to enforce continuous parameterization at session boundaries
• A priori model for hydrostatic component of troposphere (mapped with dry-GMF or VMF1 mapping function)
• Horizontal troposphere gradient parameters
• Introduction of (globally) estimated troposphere delays on normal equation level
• Ionosphere corrections from global or regional ionosphere maps
• Higher order ionosphere corrections (2nd and 3rd order and ray bending) including scaling factors

Processing

The following list of processing features and characteristics can only be a selection on the very top level. It is related to the usual processing steps and some selected product aspects:

RINEX

• Support RINEX 2.x and 3.x
• Verification of the station name with respect to the filename
• Extended checks of the RINEX headers with respect to an internal equipment database (the so-called "Station Information" file) for the receiver/antenna+radome type and number as well as the antenna height
• Check the availability of antenna+radome phase center corrections
• Automated exclusion of stations with equipment changes, or presenting a reduced data tracking, or with indicated data problems ("Problems" reported in a "Station Information" file) when importing the data.

Preprocessing

• Detection and handling of inconsistencies between code and phase data regarding the receiver clock
• Determination of unknown GLONASS frequency numbers
• Baseline selection with advanced conditions regarding GNSS, marginally observed satellites, and beneficial baselines for ambiguity resolution.
• Automated adjustment of the screening technology for the cycle slip detection and outlier rejection according to the baseline length
• Automated exclusion of data intervals with exceptionally high number of cycle slips
• Removal of satellites with unusual high number of data problems
• Automated detection of misbehaving stations/satellites based on post-fit residuals

Ambiguity resolution

• Optimized multiple-step ambiguity resolution scheme with different algorithms according to the baseline length, even for very long baselines
• Self-calibrating ambiguity resolution for GLONASS
• Consider 1/4-cycle biases resulting from the simultaneous tracking of GPS L2C and L2P signals with some receiver types
• Re-initialisation of ambiguities resolution results for all or a specific GNSS (if needed)

Processing

• Double-difference network solution with correct correlations
• Zero-difference network solution solving for all receiver and satellite clock parameters (equivalent to a consistent double difference solution)
• Precise Point Positioning (PPP)
• All three approaches are available for multi- (GPS/GLONASS/...) or single GNSS configurations for single- or dual-frequency datasets
• Numerous parameter types can be setup at the same time; generation of normal equations without inversion
• Flexible observation sampling, in particular for epoch parameters

Normal equation handling

• Geodetic datum definition
• Efficient parameter transformation for various purposes (reduce the resolution of parameters in time; long-arc for orbit determination)
• Flexible management to select parameters for pre-elimination or deletion (e.g., exclusion of boundaries of the normal equation to guarantee continuity or keep station dependent parameters for specific sites for collocation)
• Manipulations of normal equations without inversion
• Adaption of a priori values for most of the parameters
• Some parameters can be added to existing normal equations
• SINEX generation based on "normal equation" or "covariance" representation
• Computation of repeatability for parameters of the input normal equations
• Evaluation of the time series by program FODITS (Find Outliers and Discontinuities in Time Series, see PhD thesis Ostini, 2012)

Antennas

• Import of antenna phase center corrections from ANTEX format
• Receiver and satellite phase center corrections can be considered for each frequency to be processed
• Individual GNSS-specific as well as receiver antenna corrections can be introduced
• Receiver and satellite antenna phase center parameters can be estimated

Orbit modeling and estimation

• Satellite information file with the complete constellation history
• Satellite orbits import from precise orbit files depending on the accuracy code
• Estimation of six initial orbital elements and a set of up to nine dynamical orbit parameters in different frames:
  • Sun-oriented frame at the satellite center of mass
  • Flight direction oriented frame at the satellite center of mass
• All coefficients of the a priori CODE radiation pressure model are listed in the "Satellite information" file
• Estimation of stochastic pulses during the orbit integration based on precise orbit files
• Estimation of stochastic pulses or empirical accelerations from GNSS observation data during the orbit improvement
• Detection and determination of GPS repositioning events

Differential Code Biases (DCB)

• Support of P1−C1, P2−C2, and P1−P2 differential code biases
• Management of inter-system (e.g, between GPS and GLONASS) and inter-frequency (e.g., for GLONASS) code biases
• Estimation of differential code biases during parameter estimation (ionosphere or clock product generation, Melbourne-Wübbena linear combination) or directly starting from RINEX observation files

Clock product generation

• Combination of clock RINEX files
• Automated selection of a reference clock
• Extrapolation of series from clock RINEX file
• Phase-based interpolation of clock corrections to generate high-rate clock products

Simulation of GNSS observations

• According to a given geometry (satellite orbits and station positions) GNSS observations can be computed
• Assumptions with respect to the noise level, receiver/satellite clocks, ionosphere, troposphere, and cycle slips can be introduced
• These synthetic data can be processed on zero-difference or double-difference level
• The correct integer ambiguity is known to be zero in case of ambiguity resolution tests