Carrier Phase Integer Ambiguity Resolution

Teunissen, Peter

doi:10.1007/978-3-319-42928-1_23

Cited by 20 publications

(10 citation statements)

References 44 publications

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“…In detail:ρsj=ρ0sj+c·dt+ηρsj0.166667em, where the geometric range between the receiver and the satellite sj is denoted by ρ0sj, while dt and ηρsj stand for the receiver clock offset and the sum of code measurement errors with respect to satellite sj, respectively. For the carrier phase observations, we have (in meters) [24]:ϕsj=ρ0sj+c·dt+λAsj+ηϕsj where λ, Aϕ, and ηϕsj are the carrier wavelength, the phase ambiguity, and the sum of carrier measurement errors with respect to satellite sj, respectively.…”

Section: Methods For Measuring and Modeling Errors Of A Gnss Receivermentioning

confidence: 99%

Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS Receiver Performance

Lehtola

Söderholm

Koivisto

et al. 2019

Sensors

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GNSS receiver data crowdsourcing is of interest for multiple applications, e.g., weather monitoring. The bottleneck in this technology is the quality of the GNSS receivers. Therefore, we lay out in an introductory manner the steps to estimate the performance of an arbitrary GNSS receiver via the measurement errors related to its instrumentation. Specifically, we do not need to know the position of the receiver antenna, which allows also for the assessment of smartphone GNSS receivers having integrated antennas. Moreover, the method is independent of atmospheric errors so that no ionospheric or tropospheric correction services provided by base stations are needed. Error models for performance evaluation can be calculated from receiver RINEX (receiver independent exchange format)data using only ephemeris corrections. For the results, we present the quality of different receiver grades through parametrized error models that are likely to be helpful in stochastic modeling, e.g., for Kalman filters, and in assessing GNSS receiver qualities for crowdsourcing applications. Currently, the typical positioning precision for the latest smartphone receivers is around the decimeter level, while for a professional-grade receiver, it is within a few millimeters.

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Section: Methods For Measuring and Modeling Errors Of A Gnss Receivermentioning

confidence: 99%

Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS Receiver Performance

Lehtola

Söderholm

Koivisto

et al. 2019

Sensors

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“…Several integer ambiguity resolution methods have been proposed including the integer rounding, integer bootstrapping, integer least squares, and partial ambiguity resolution methods [64]. The integer rounding approach is used in this research to resolve the WL and NL ambiguities.…”

Section: Integer Ambiguity Validationmentioning

confidence: 99%

GRACE-FO Antenna Phase Center Modeling and Precise Orbit Determination with Single Receiver Ambiguity Resolution

Jin

Jiang

et al. 2021

Remote Sensing

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Precise knowledge of the phase center location of the global navigation satellite system (GNSS) antenna is a prerequisite for precise orbit determination (POD) of the low Earth orbit (LEO) satellite. The phase center offset (PCO) and phase center variation (PCV) values for the LEO antenna obtained from ground calibration cannot reflect the error sources encountered in the actual spacecraft environment. PCV corrections are estimated by ionosphere free (IF) carrier phase post-fit residuals of reduced dynamic orbit determination. Ambiguity resolution (AR) plays a crucial role in achieving the best orbit accuracy. The single receiver AR concept is realized using wide-lane (WL) and narrow-lane (NL) bias products. Single difference (SD) observations between satellites are applied to remove the receiver dependent phase bias. SD AR and traditional double difference (DD) AR methods are applied to fix the ambiguities. The recovered SD and DD IF ambiguities are taken as pseudo-observations to constrain the undifferenced IF ambiguity parameters in the POD process. The LEO orbits based on float ambiguity (FA), SD, AR, and DD AR are investigated. One year’s data collected by the Gravity Recovery And Climate Experiment Follow-On (GRACE-FO) mission and GPS precise products provided by the Center for Orbit Determination in Europe (CODE) were analyzed. Precise orbit generated by the Jet Propulsion Laboratory (JPL), independent satellite laser ranging (SLR), and K-band ranging (KBR) measurements were utilized to assess the orbit accuracy. More than 98% of SD WL and 95% of SD NL ambiguities are fixed, which confirms the good quality of the bias products and correctness of the SD AR method. With PCV corrections, the average phase residuals of DD and SD AR solutions are 0.13 and 0.41 mm, which indicates improved consistency between applied models and observations. Compared with JPL’s orbit, the SD AR orbits achieve the accuracy of 6.0, 6.2, and 5.1 mm in along-track, cross-track, and radial directions. The SD AR solutions show an average improvement of 18.3% related to the FA orbits while 6.3% is gained by the DD AR approach. The root mean squares (RMSs) of SLR residuals for FA, DD AR, and SD AR solutions are 11.5, 10.2, and 9.6 mm, which validate the positive effect of AR on POD. Standard deviation (STD) of KBR residuals for SD AR orbits is 1.8 mm while 0.9 mm is achieved by the DD AR method. The explanation is that the phase bias products used for SD AR are not free of errors and the errors may degrade the KBR validation. In-flight PCV calibration and ambiguity resolution improve the LEO orbit accuracy effectively.

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“…Processing carrier phase observations in baseline mode or as a single station in precise point positioning (PPP) mode requires the estimation of carrier phase ambiguities. The ambiguities of the original signals, L1 and L2, or of linear combinations of these signals, are either treated as real numbers (float solution) or fixed to their true integer values (fixed solution) [7].…”

Section: L2 Quarter-cycle Bias and Its Effect On Ambiguity Estimationmentioning

confidence: 99%

Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations

Wanninger

2018

Sensors

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Presently, the global positioning system (GPS) satellite constellation consists of 40% older Block IIA and IIR space vehicles and 60% newer IIR-M and IIF satellites. Only newer GPS satellites are capable of transmitting the L2C signal which is in quadrature to the legacy L2P(Y) signal being broadcast by all satellites. The data format RINEX-2 is not prepared to contain carrier phase observations of both L2 signals, but should contain either one or the other. If a mix of unaligned L2P(Y) and L2C carrier phase observations are stored in a RINEX-2 file, the quarter cycle bias causes the file to be defective and not usable for precise positioning purposes. Algorithms that detect such files are presented in this study. They are mainly based on the analysis of widelane fractional ambiguities and were applied to RINEX-2 files of 2624 reference stations. Seventy-two station files (2.7%) were found to be defective since they contained mixed and unaligned L2P(Y) and L2C carrier phase observations. If such files are used for precise positioning with ambiguities being fixed to integer values, resulting coordinate errors in long baselines can reach centimeter levels. Unaligned L2 observations often prevent ambiguity fixing, especially in short baselines.

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Carrier Phase Integer Ambiguity Resolution

Cited by 20 publications

References 44 publications

Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS Receiver Performance

Exploring GNSS Crowdsourcing Feasibility: Combinations of Measurements for Modeling Smartphone and Higher End GNSS Receiver Performance

GRACE-FO Antenna Phase Center Modeling and Precise Orbit Determination with Single Receiver Ambiguity Resolution

Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations

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