فهرست مطالب simkooei

More than two decades, continuous observations of permanent GPS stations used extensively for behavior of geodynamic phenomena have found such as deformation of the earth's crust, moving tectonic plates and fault. Daily position of permanent GPS stations are considered independent of each other in statistic. In other hand, some errors such as the satellite orbit modeling errors, determining parameters of rotation's earth, parameters of atmospheric modeling and etc; cause color noise or the correlation between daily positions of stations. Another important systematic error in GPS time series is offset. Offset by factors such as earthquakes, replace the GPS antenna, human and environmental errors are generated. Offset in GPS time series's functional model causes the bias estimation of unknown parameters Therefor, we require exact statistical model and function model of GPS time series to exact estimation of the speed parameter. For this purpose, multivariate noise analysis on 38 permanent GPS stations of Iran, were carried out by the period of 7 years (2006–2012), in this study. These stations are scattered throughout the country. In this analysis, statistical model of data presented of by incorporating white noise, flicker noise and random Walk noise and noise components estimation has taken place by method of " least squares variance component estimation ". Also the effect of offset in the data were examined on estimation of noise and station's speed parameters by method of "least squares". The temporal correlation with multivariate noise analysis was performed for both single-station and multi- station. The results showed that noise amounts in coordinate series distributed due to processing all stations with each other in multi-station mode. In multivariate analysis single-station, Random Walk noise amounts after offset removal get zero in some stations, but in multi- station mode any stations were not zero for Random Walk noise. So single- station mode is more realistic than multi- station mode. Noises estimation from data with offset and compare them with results from data without offset shows offset effect on the amounts of noise, especially Random Walk noise that the greater part of it consist of offset. In addition to consideration of offset effect on noise, its effect on the speed parameters were also assessed. This assessment shows changes of length and direction of speed vector, after removing offset that examine necessity of offset's studying at data . So having the correct functional model and statistical model and without offset in the exact determination of parameters such as speed is essential. The studying of spatial correlation showed that there are significant correlation for components location North - North, East - East and height - height after removing offset, but correlation results are not significant between the various components of the coordinate, North - East, North - height and East - height. After removing offset of data, difference of speed vector from its average value, was calculated. It's found, speed of stations in tectonic boundaries (Saudi Arabia in the south and Eurasia tectonic plates in the north of Iran) more than center of Iran.

In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is the detection and elimination of outliers, which may appear to be inconsistent with the remainder of the observations or the model. Outlier detection is thus a primary step in many geodetic applications. There are various methods in handling the outlying observations among which a sequential data snooping procedure, known as Detection, Identification and Adaptation (DIA) algorithm, is employed in the present contribution. An efficient data snooping procedure is based on the Baarda’s theory in which blunders are detected element-wise and the model is adopted in an iterative manner. This method may become computationally expensive when there exists a large number of blunders in the observations. An attempt is made to optimize this commonly used method for outlier detection. The optimization is performed to improve the computational time and complexity of the conventional method. An equivalent formulation is thus presented in order to simplify the elimination of outliers from an estimation set-up in a linear model. The method becomes more efficient when there is a large number of model parameters involved in the inversion. In the conventional method this leads to a large normal matrix to be inverted in a consecutive manner. Based on the recursive least squares method, the normal matrix inversion is avoided in the presented algorithm. The accuracy and performance of the proposed formulation is validated based on the results of two real data sets. The application of this formulation has no numerical impact on the final result and it is identical to the conventional outlier elimination. The method is also tested in a simulation case to investigate the accuracy of the outlier detection method in critical cases when large amount of the data is contaminated. In the application considered, it is shown that the proposed algorithm is faster than the conventional method by at least a factor of 3. The method becomes faster when the number of observations and parameters increases.

Due to wide spread usage of the satellite positioning techniques especially GPS, we need to precisely determine geoid model in order to use GPS measurements for height determination, as an alternative of traditional leveling techniques in geodetic applications. Precise local geoid modelling using GPS/Leveling data, apart from the existing models such as geopotential models and gravimetric geoid models could be an interesting investigation topic. An important question is, ‘What accuracy level can be achieved using this approach?’ However precession of this modelling could be influenced by some issues such as data quality or modelling techniques. In this paper, we attempt to assess the implementation of modern learning-based computing techniques including artificial neural networks and adaptive network-based fuzzy inference systems compared with multivariate polynomial regression equations in GPS/Leveling Geoid modeling. This assessment carried out in a small and dense network of GPS/Leveling benchmarks in contrast with previous studies, located in shahin-shahr, Isfahan. And these high quality data make it possible to achieve an accuracy of better than 1 cm. The results show a few millimeter superiority of ANN and ANFIS derived geoid models in terms of root mean square error, as well as in terms of coefficient of determination. And RMSE=8cm, R2=0.9949 and RMSE=7cm, R2=0.9964 achieved for this models respectively. Therefore ANFIS derived geoid model provide the most accurate geoid heights in the study area.

One important source of errors on GNSS signals is the ionospheric effect. This layer of the atmosphere is filled with charged particles. Ionospheric effects on the waves are dependent on the amount of TEC. This paper uses the least square harmonic estimation (LS-HE) that is one of the analytical methods in the frequency domain. We used the vertical TEC values obtained from GIM models with global cover provided by the JPL analysis center. We use 15 years of bihourly data gathered from the 152th day in 1998 to the first day of 2014. We first determine the important periodic signals by applying the univariate and multivariate harmonic estimate on the TEC time series. The multivariate analysis revealed the presence of daily periodic signal with its higher harmonics and annual period with its higher harmonics. We then calculate the spectral power of a number of identified signals in all available data range. The result indicate that the higher harmonics of the daily signal (tri and quad diurnal) show their maximum spectral values in the dip equator. This indicates that the earth's magnetic field is one of the cause, to these provide patterns.

In most applications of GPS time series, white noise is considered as a covariance matrix, while over 20 years of GPS time series, white noise is not enough for unwanted signals and on the other hand white noise is unavoidable part of realistic noise model. Selecting of a prepare noise model assure us to considering all unwanted signals which GPS have been involved during its time series. In order of comparison between two modes, white noise and realistic noise, we considered one of the most important of application of GPS time series. For this purpose, strain analysis was selected, but, in additional of considering noise model, for better comparison, we introduces Normalized Strains which could help us for the comparison in two different states, realistic noise and white noise. California Network with more than 10years continuous GPS data with proper density is appropriate criteria to assessment of efficacy of time series duration and No discontinuity GPS data on final results. Beside, Azerbaijan Network is one of the most regular and most dense of Iranian Networks which its obtained noise model can be considered as Iranian Geodynamic Network’s noise model. By comparing of these two networks results, there are some useful suggestions to improve the Iranian Geodynamic Network’s future outcomes.

To estimate the unknown parameters in a linear model in which the observations are linear functions of the unknowns, one of the conventional methods is the least-square estimation. The best linear unbiased estimation (BLUE) is achieved when the inverse of the variancecovariance matrix of the observables is considered as the weight matrix in the estimation process. Therefore having a realistic assessment of the precision of the observations is an important issue. One of the methods to reach this goal is the use of the least-square variance component estimation (LS-VCE). However, in this method, it is not impossible to estimate negative variances. But, they are not acceptable from the statistical point of view. In this paper, numerical methods such as genetic algorithm and also iterative methods based on LS-VCE are presented for non-negative estimation of variance components. By using non-negative variance components estimation methods not only one guarantees the non-negative variance components but also one can investigate to incorporate different noise components into the stochastic model. Those components that are not likely present are automatically estimated zeros. In this paper, using the above-mentioned methods, we assess the noise characteristics of time series of GPS permanent stations. The data used in this research are the coordinates of IGS stations located in Mehrabad-Tehran and also two other stations in Ahvaz and Mashhad (2005-2010). To deal with this amount of data, the iterative methods are superior over the numerical methods such as the genetic algorithm. The results indicate the noise of GPS position time series are a combination of white noise plus flicker noise, and in some cases combined with random walk noise.

Concepts of multiple reference stations—Virtual Reference Station (VRS) for instance—have been developed during the last two decades to overcome the traditional RTK deficiencies. The basic idea behind the VRS method is the application of spatiotemporal dependence of errors to reduce the effects of biases on virtually generated observations. This amelioration improves the final coordinates and reduces the number of permanent stations settlement over a regional network. The virtual station must be as close as possible to the rover station. For this purpose, the rovers have to transmit their approximate coordinates to a data process center (the master reference station), which can be obtained using the navigation solution. The approximate coordinates of the rover are then selected as the position of the virtual station. Therefore the virtual observations can be generated at the VRS position and corrections due to the dispersive and nondispersive biases are implemented to the observations. This generation of virtual observations improves the final coordinates and reduces the number of permanent stations settlement over a regional network. In this article the VRS generation algorithm is developed and applied to six GPS stations of NGS (National Geodetic Survey) permanent network. Various error interpolation methods are tested for the VRS algorithm efficiency. The results prove that the VRS algorithm works correctly, which can be used for regional and national networks. The results were shown not much to be dependent on the choice of the interpolation method. However, the error mitigation algorithm plays the most important role.

Our purpose in this contribution is to compare different outlier detection methods as far as time series are concerned. In fact, three methods, namely wavelet analysis, Baarda data snooping and thresholding are investigated. In order to make reasonable comparisons among the performance of these three methods in detecting the outliers, we used 4-month synthesized time series based on real tidal data. When the functional model of observations is known, Baarda data snooping, in comparison with other two methods yields the best results since its outlier rate of success and outlier rate of failure are almost 100% and 0.64%, respectively, regardless of the type of outliers. Furthermore, if the functional model of observations is unknown, wavelet analysis perform better than thresholding.

Inertial Navigation System (INS) and Global Positioning System (GPS), are used in various navigation and positioning applications. Because each of the INS and GPS technologies has some limitations and advantages, during last two decades, the systems integration has been widely used for accurate and reliable navigation and positioning. In an integrated system, accurate GPS observations are used to estimate the high rate INS errors and state vector (including INS error vector, position, velocity and other optional parameters). A field test results are presented in this paper. The goal of this test is to compare the coordinates of a relatively low cost INS, GPS RTK coordinates, and the integrated GPS/INS results. The decentralized approach has been used for this integration.

Deformation survey using geodetic techniques for large buildings and landslides monitoring can be performed using multiple epoch’s method. The first step is the design of a reliable monitoring network. To compute the displacement, a coordinate system (datum) is to be established. Such a system is defined over a few points that are assumed to be stable during the campaign. We therefore require an efficient method for stable points’ detection. The simultaneous adjustment method is presented in the present contribution. L1 norm minimization method and global congruency test are the other two methods for this purpose. We aim to study the performance and sensitivity of simultaneous adjustment to identify the stable and unstable points using simulated data at four epochs. This method is then compared with the L1 norm method and the global congruency test for simulated and real data at two epochs. The results illustrates that increasing the observation epochs augments the ability of deformation analysis and stable points detection for simultaneous adjustment. In addition, simultaneous adjustment provides superior results over small displacements compared to the other two methods.