Delay Prediction Model Research Articles

Evaluation of EGNOS Tropospheric Delay Model in South-Eastern Europe

The troposphere affects electromagnetic signal propagation causing signal path bending and the alteration of the electromagnetic wave velocity. Tropospheric delay can introduce a considerable error in satellite positioning if it is not properly estimated. The GPS signal delay can vary from 2 to 20 m depending on the elevation angles between the receiver and the satellite. Two basic types of delay prediction models exist. The first use surface meteorological parameters to estimate the value of the tropospheric delay, and the other models that do not require real-time meteorological input use average and seasonal variation data related to the receiver's latitude and day-of-year. This paper compares the performance of both types of model over a period of one year, comprising all seasons, to verify their accuracy over a longer period. The Saastamoinen model, known as one of the best performing prediction models, was taken as a reference and the global EGNOS model was used to check how the global estimates of the yearly averages of the meteorological parameters and their related seasonal variations comply with the real-time surface parameters.

Estimating Flight Departure Delay Distributions—A Statistical Approach With Long-Term Trend and Short-Term Pattern

In this article we develop a model for estimating flight departure delay distributions required by air traffic congestion prediction models. We identify and study major factors that influence flight departure delays, and develop a strategic departure delay prediction model. This model employs nonparametric methods for daily and seasonal trends. In addition, the model uses a mixture distribution to estimate the residual errors. To overcome problems with local optima in the mixture distribution, we develop a global optimization version of the expectation–maximization algorithm, borrowing ideas from genetic algorithms. The model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We use flight data from United Airlines and Denver International Airport from the years 2000/2001 to train and validate our model.

Estimating Flight Departure Delay Distributions - A Statistical Approach With Long-Term Trend and Short-Term Pattern

In this paper, we develop a model for estimating flight departure delay distributions required by air traffic congestion prediction models. We identify and study major factors influencing flight departure delays, and develop a strategic departure delay prediction model. This model employs nonparametric methods for daily and seasonal trends. In addition, the model uses a mixture distribution to estimate the residual errors. In order to overcome problems with local optima in the mixture distribution, we develop a global optimization version of the Expectation Maximization algorithm, borrowing ideas from Genetic Algorithms. The model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We use flight data from United Airlines and Denver International Airport from the years 2000/01 to train and validate our model.

Predicting atmospheric biases for real-time ambiguity resolution in GPS/GLONASS reference station networks

When using multiple reference station networks to support real-time kinematic positioning, the global positioning system (GPS) and/or GLONASS carrier-phase ambiguities associated with between-reference-receiver processing have to be resolved. At the initialisation stage it is not difficult to reliably resolve these ambiguities, in the post-processing mode, using data sets of several hours or days in length. However, due to significant residual atmospheric delays in the double-differenced measurements, it is a big challenge to resolve the ambiguities within the reference station network in real time, particularly in the case of the ambiguities of the newly risen satellites. Both temporal and spatial correlation characteristics for the atmospheric delays are discussed, and appropriate atmospheric delay prediction models are proposed. The experimental results show that the proposed bias prediction models can be used to reliably and efficiently resolve the integer ambiguities within reference station ne tworks in real time, on a single-epoch basis.

Tropospheric Zenith Delay Prediction Accuracy for High-Precision GPS Positioning and Navigation

: In this paper, we determine mean bias and root-mean-square (RMS) scatter for a large number of zenith tropospheric propagation delay prediction models developed in the last few decades by comparing the models against ray-tracing results using a 1-year data set of radiosonde profiles. We conclude that the hydrostatic zenith delay can be predicted with submillimeter accuracy, provided that accurate measurements of station pressure are available. For wet zenith delay, the models differ significantly in accuracy, but show very similar RMS scatter. Our analyses show that the wet zenith delay can typically be predicted with a precision of approximately 3 cm using meteorological data. The prediction of the total delay by models typically used in airborne navigation indicates a much poorer accuracy, leading to prediction biases ranging from around 6 cm to more than 20 cm. In general, all the models tested perform significantly better at midlatitudes than at low latitudes.

Analysis of tropospheric delay prediction models: comparisons with ray-tracing and implications for GPS relative positioning

Ray-tracing is used to examine the accuracy of several well known models for tropospheric delay prediction under varying atmospheric conditions. The models considered include the Hopfield zenith delay model and related mapping functions, the Saastamoinen zenith delay model and mapping function, and three empirical mapping functions based upon the Marini continued fraction form. Modelled delays are benchmarked against ray-tracing solutions for representative atmospheric profiles at various latitudes and seasons. Numerical results are presented in light of the approximations inherent in model formulation. The effect of approximations to the temperature, pressure and humidity structure of the neutral atmosphere are considered; the impact of surface layer anomalies (i.e., inversions) on prediction accuracy is examined; and errors resulting from the neglect of ray bending are illustrated. The influence of surface meteorological parameter measurement error is examined. Finally, model adaptability to local conditions is considered. Recommendations concerning the suitability of the models for GPS relative positioning and their optimal application are made based upon the results presented.