Review and principles of PPP-RTK methods

PPP-RTK is integer ambiguity resolution-enabled precise point positioning. In this contribution, we present the principles of PPP-RTK, together with a review of different mechanizations that have been proposed in the literature. By application of S-system theory, the estimable parameters of the different methods are identified and compared. Their interpretation is essential for gaining a proper insight into PPP-RTK in general, and into the role of the PPP-RTK corrections in particular. We show that PPP-RTK is a relative technique for which the ‘single-receiver user’ integer ambiguities are in fact double-differenced ambiguities. We determine the transformational links between the different methods and their PPP-RTK corrections, thereby showing how different PPP-RTK methods can be mixed between network and users. We also present and discuss four different estimators of the PPP-RTK corrections. It is shown how they apply to the different PPP-RTK models, as well as why some of the proposed estimation methods cannot be accepted as PPP-RTK proper. We determine analytical expressions for the variance matrices of the ambiguity-fixed and ambiguity-float PPP-RTK corrections. This gives important insight into their precision, as well as allows us to discuss which parts of the PPP-RTK correction variance matrix are essential for the user and which are not

DIA-datasnooping and identifiability

In this contribution, we present and analyze datasnooping in the context of the DIA method. As the DIA method for the detection, identification and adaptation of mismodelling errors is concerned with estimation and testing, it is the combination of both that needs to be considered. This combination is rigorously captured by the DIA estimator. We discuss and analyze the DIA-datasnooping decision probabilities and the construction of the corresponding partitioning of misclosure space. We also investigate the circumstances under which two or more hypotheses are nonseparable in the identification step. By means of a theorem on the equivalence between the nonseparability of hypotheses and the inestimability of parameters, we demonstrate that one can forget about adapting the parameter vector for hypotheses that are nonseparable. However, as this concerns the complete vector and not necessarily functions of it, we also show that parameter functions may exist for which adaptation is still possible. It is shown how this adaptation looks like and how it changes the structure of the DIA estimator. To demonstrate the performance of the various elements of DIA-datasnooping, we apply the theory to some selected examples. We analyze how geometry changes in the measurement setup affect the testing procedure, by studying their partitioning of misclosure space, the decision probabilities and the minimal detectable and identifiable biases. The difference between these two minimal biases is highlighted by showing the difference between their corresponding contributing factors. We also show that if two alternative hypotheses, say (Formula presented.) and (Formula presented.), are nonseparable, the testing procedure may have different levels of sensitivity to (Formula presented.)-biases compared to the same (Formula presented.)-biases

Minimal Detectable and Identifiable Biases for quality control

The Minimal Detectable Bias (MDB) is an important diagnostic tool in data quality control. The MDB is traditionally computed for the case of testing the null hypothesis against a single alternative hypothesis. In the actual practice of statistical testing and data quality control, however, multiple alternative hypotheses are considered. We show that this has two important consequences for one's interpretation and use of the popular MDB. First, we demonstrate that care should be exercised in using the single-hypothesis-based MDB for the multiple hypotheses case. Second, we show that for identification purposes, not the MDB, but the Minimal Identifiable Bias (MIB) should be used as the proper diagnostic tool. We analyse the circumstances that drive the differences between the MDBs and MIBs, show how they can be computed using Monte Carlo simulation and illustrate by means of examples the significant differences that one can experience between detectability and identifiability

Five-frequency Galileo long-baseline ambiguity resolution with multipath mitigation

© 2018, The Author(s). For long-baseline over several hundreds of kilometers, the ionospheric delays that cannot be fully removed by differencing observations between receivers hampers rapid ambiguity resolution. Compared with forming ionospheric-free linear combination using dual- or triple-frequency observations, estimating ionospheric delays using uncombined observations keeps all the information of the observations and allows extension of the strategy to any number of frequencies. As the number of frequencies has increased for the various GNSSs, it is possible to study long-baseline ambiguity resolution performance using up to five frequencies with uncombined observations. We make use of real Galileo observations on five frequencies with a sampling interval of 1 s. Two long baselines continuously receiving signals from six Galileo satellites during corresponding test time intervals were processed to study the formal and empirical ambiguity success rates in case of full ambiguity resolution (FAR). The multipath effects are mitigated using the measuremen ts of another day when the constellation repeats. Compared to the results using multipath-uncorrected Galileo observations, it is found that the multipath mitigation plays an important role in improving the empirical ambiguity success rates. A high number of frequencies are also found to be helpful to achieve high ambiguity success rate within a short time. Using multipath-uncorrected observations on two, three, four and five frequencies, the mean empirical success rates are found to be about 73, 88, 91, and 95% at 10 s, respectively, while the values are increased to higher than 86, 95, 98, and 99% after mitigating the multipath effects

IRNSS/NavIC and GPS: a single- and dual-system L5 analysis

The Indian Regional Navigation Satellite System (IRNSS) has recently (May 2016) become fully operational. In this contribution, for the fully operational IRNSS as a stand-alone system and also in combination with GPS, we provide a first assessment of L5 integer ambiguity resolution and positioning performance. While our empirical analyses are based on the data collected by two JAVAD receivers at Curtin University, Perth, Australia, our formal analyses are carried out for various onshore locations within the IRNSS service area. We study the noise characteristics (carrier-to-noise density, measurement precision, time correlation), the integer ambiguity resolution performance (success rates and ambiguity dilution of precision), and the positioning performance (ambiguity float and ambiguity fixed). The results show that our empirical outcomes are consistent with their formal counterparts and that the GPS L5-data have a lower noise level than that of IRNSS L5-data, particularly in case of the code data. The underlying model in our assessments varies from stand-alone IRNSS (L5) to IRNSS (Formula presented.) GPS (L5), from unconstrained to height-constrained and from kinematic to static. Significant improvements in ambiguity resolution and positioning performance are achievable upon integrating L5-data of IRNSS with GPS

Factoren die de overleving van weidevogelkuikens beïnvloeden

Uit onderzoek naar de effectiviteit van het huidige weidevogelbeheer komt telkens naar voren dat er voor de ontwikkeling van een beter beheer op een aantal punten nog onvoldoende kennis aanwezig is over factoren die van belang zijn voor de overleving van weidevogelkuikens. Die ontbrekende kennis spitst zich toe op de invloed die weer, voedsel en beheer op de conditie en daarmee op de overleving van kuikens hebben. Een deel van de vragen kan alleen worden beantwoord door nieuw opgezet veldwerk, maar een deel kan wel beantwoord worden op grond van nieuwe analyses en bestaande datasets . De belangrijkste uitkomsten van die analyses worden hieronder beschreven