Cycle Slip and Clock Jump Repair with Multi-Frequency Multi-Constellation GNSS data for Precise Point Positioning
Detecting and repairing cycle slips and clock jumps are crucial data pre-processing steps when performing Precise Point Positioning (PPP). If left unrepaired, cycle slips and clock jumps can adversely affect PPP convergence time, accuracy and precision. This paper proposes algorithms for detecting a...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/23233 |
| Summary: | Detecting and repairing cycle slips and clock jumps are crucial data pre-processing steps when performing Precise Point Positioning (PPP). If left unrepaired, cycle slips and clock jumps can adversely affect PPP convergence time, accuracy and precision. This paper proposes algorithms for detecting and repairing cycle slips and clock jumps using multi-constellation and multi-frequency (MCMF) GNSS data. It is shown that availability of a third frequency enables reliable validation of detected cycle slips. This is because triple frequency analysis can identify the frequency on which the cycle slip occurred as part of the detection process. A clock jump detection and repair procedure is also proposed for a receiver with both carrier phase and code measurements showing jumps. The proposed method uses the average code and phase linear combination and applies to static data. A spline function is used to approximate the data for a pre-defined time window prior to each measuring epoch and a test is performed for detecting presence of a clock jump by comparing the interpolated value to measured value. The algorithm can effectively determine clock jumps for single frequency data from a single constellation as well as MCMF GNSS data. However, MCMF GNSS data adds redundancy, hence improves the reliability of the clock jump detection algorithm. It is recommended to detect and repair clock jumps when using PPP to allow improved modelling of the receiver clock offset in the dynamic model. |
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