Gotowe bibliografie tematyczne / Autocorrelation function (ACF)

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Gotowa bibliografia na temat „Autocorrelation function (ACF)”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 12 lutego 2022

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Artykuły w czasopismach na temat "Autocorrelation function (ACF)"

LEE, CHOONG-HOON, HEUNG-KYU LEE, and YOUNGHO SUH. "IMAGE WATERMARKING RESISTANT TO COMBINED GEOMETRIC AND REMOVAL ATTACKS." International Journal of Image and Graphics 05, no. 01 (January 2005): 37–65. http://dx.doi.org/10.1142/s0219467805001653.

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In this paper, we propose an improved autocorrelation function (ACF)-based watermarking that is robust against combined geometric and removal attacks. ACF-based watermarking is known to be effective against geometric attacks. In this watermarking scheme, both the embedded watermark and autocorrelation peaks of the watermark should survive a variety of attacks. However, the autocorrelation peaks are not strong enough. The proposed method improves the robustness of ACF-based watermarking by enhancing the strength of the autocorrelation peaks. Instead of the simple addition of a watermark to an image, the proposed watermark embedder uses the information of an original image more actively during the embedding process, so that the marked image has strong autocorrelation peaks. Experimental results show that the proposed scheme provides better robustness than conventional ACF-based watermarking against a variety of combined geometric-removal attacks.

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Bykhovsky, Dima, and Tom Trigano. "Numerical Generation of Compound Random Processes with an Arbitrary Autocorrelation Function." Fluctuation and Noise Letters 17, no. 01 (January 23, 2018): 1850001. http://dx.doi.org/10.1142/s0219477518500013.

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The generation of non-Gaussian random processes with a given autocorrelation function (ACF) is addressed. The generation is based on a compound process with two components. Both components are solutions of appropriate stochastic differential equations (SDEs). One of the components is a Gaussian process and the other one is non-Gaussian with an exponential ACF. The analytical study shows that a compound combination of these processes may be used for the generation of a non-Gaussian random process with a required ACF. The results are verified by two numerical examples.

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Li, Ming, and Wei Zhao. "Convergence of Sample Autocorrelation of Long-Range Dependent Traffic." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/725730.

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We depict our work on a fundamental issue in the theory of long-range dependent traffic in the aspect of the convergence of sample autocorrelation function (ACF) of real traffic. The present results suggest that the sample ACF of traffic is convergent. In addition, we show that the sample size has considerable effects in estimating the sample ACF of traffic. More precisely, a sample ACF of traffic tends to be smoother when the sample size increases.

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Korobeinikov, A. V. "Fast algorithm for calculating autocorrelation function in code synthesis tasks by enumerative technique." Issues of radio electronics 1, no. 1 (March 13, 2021): 13–18. http://dx.doi.org/10.21778/2218-5453-2021-1-13-18.

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The fast algorithm for calculating the autocorrelation function (ACF) of a binary code is developed in relation to the problem of synthesizing codes with a given ACF by enumerative technique. The algorithm is applicable for any duration of the N code. The computational complexity of calculating the ACF is 2N multiplication operations and 2N addition operations. The linear dependence of computational complexity on the duration of the N code is noted. To calculate the ACF of the newly created code combination, the previous code, its ACF, and the index of the changed code element are used. The condition of applicability of the algorithm is that the search of code combinations must be performed by changing only one element in the current code combination. An enumerative technique is proposed that allows a complete enumertion of all 2N existing combinations by sequentially changing the code combination of just one element.

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Li, Ming, and Wei Zhao. "Smoothing the Sample Autocorrelation of Long-Range-Dependent Traffic." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/631498.

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This paper depicts our work in smoothing the sample autocorrelation function (ACF) of traffic. The experimental results exhibit that the sample ACF of traffic may be smoothed by the way of average. In addition, the results imply that the sum of sample ACFs of traffic convergences. Considering that the traffic data used in this research is long-range dependent (LRD), the latter may be meaningful for the theoretical research of LRD traffic.

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EL-KHAMY, S. E., M. M. HADHOUD, M. I. DESSOUKY, B. M. SALAM, and F. E. ABD EL-SAMIE. "ACQUISITION OF SARSAT INFORMATION USING AN AUTO-CORRELATION BASED ADAPTIVE LINE ENHANCER." International Journal of Information Acquisition 02, no. 04 (December 2005): 291–302. http://dx.doi.org/10.1142/s0219878905000659.

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This paper develops a new approach to the detection of the emergency locator transmitter (ELT) signal using an adaptive line enhancer (ALE). The input signal to the ALE is replaced by its autocorrelation function (ACF) because noise affects the central samples of the autocorrelation function only while it affects all samples of the noisy signal. This gives the ALE the ability to get rid of noise, easily. The output ACF is then used in the spectral estimation and detection of the ELT signal. This approach is related to the signal processing using Higher Order Statistics (HOS) since the ACF is the used input. The paper also compares the results of the new approach to other different previously used methods foe ELT signal detection. The first method uses the signal xk as an input to the ALE, and the second method uses [Formula: see text]. Results illustrate the superiority of the proposed method over the other two methods. A comparison study between the performance of two types of ALE; Fixed pole radius and variable pole radius ALEs in detecting the ELT signal, is introduced.

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ANISHCHENKO, VADIM S., TATJANA E. VADIVASOVA, ANDREY S. KOPEIKIN, GALINA I. STRELKOVA, and JÜRGEN KURTHS. "SPECTRAL AND CORRELATION ANALYSIS OF SPIRAL CHAOS." Fluctuation and Noise Letters 03, no. 02 (June 2003): L213—L221. http://dx.doi.org/10.1142/s0219477503001282.

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We study numerically the behavior of the autocorrelation function (ACF) and the power spectrum of spiral attractors without and in the presence of noise. It is shown that the ACF decays exponentially and has two different time scales. The rate of the ACF decrease is defined by the amplitude fluctuations on small time intervals, i.e., when τ < τ cor , and by the effective diffusion coefficient of the instantantaneous phase on large time intervals. It is also demonstrated that the ACF in the Poincare map also decreases according to the exponential law exp (- λ+ k), where λ+ is the positive Lyapunov exponent. The obtained results are compared with the theory of fluctuations for the Van der Pol oscillator.

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Li, Ming. "Power Spectrum of Generalized Fractional Gaussian Noise." Advances in Mathematical Physics 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/315979.

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Recently, we introduced a type of autocorrelation function (ACF) to describe a long-range dependent (LRD) process indexed with two parameters, which takes standard fractional Gaussian noise (fGn for short) as a special case. For simplicity, we call it the generalized fGn (GfGn). This short paper gives the power spectrum density function (PSD) of GfGn.

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Shitan, Mahendran, and Shelton Peiris. "A Note on the Properties of Generalised Separable Spatial Autoregressive Process." Journal of Probability and Statistics 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/847830.

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Spatial modelling has its applications in many fields like geology, agriculture, meteorology, geography, and so forth. In time series a class of models known as Generalised Autoregressive (GAR) has been introduced by Peiris (2003) that includes an index parameterδ. It has been shown that the inclusion of this additional parameter aids in modelling and forecasting many real data sets. This paper studies the properties of a new class of spatial autoregressive process of order 1 with an index. We will call this aGeneralised Separable Spatial Autoregressive(GENSSAR) Model. The spectral density function (SDF), the autocovariance function (ACVF), and the autocorrelation function (ACF) are derived. The theoretical ACF and SDF plots are presented as three-dimensional figures.

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Chui, Ming Wei, You Qian Feng, Wei Wang, Pei Lin Li, and Zheng Chao Li. "Numerical Simulation of Rough Surface with Crossed Texture." Applied Mechanics and Materials 321-324 (June 2013): 196–200. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.196.

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To meet the demands for rough surfaces data in the research of surface engineering, contacts characteristic and so on, a new numerical simulation of rough surface is proposed. Based on FFT method, rough surface with single direction texture is simulated with circular cosine-exponent autocorrelation function (ACF), and the generated surface is rotated of different given angles respectively by rotation of reference system, then the rough surface with multi-direction texture is created by synthesizing the rotated surfaces. The simulation results show that, the ACF curves of generated surface is periodic fluctuation decay, and has a good fitting result with the predetermined ACF. The contrast result between simulated surface and measured surface shows the available of the proposed method.

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Rozprawy doktorskie na temat "Autocorrelation function (ACF)"

Shang, Lei, and lei shang@ieee org. "Modelling of Mobile Fading Channels with Fading Mitigation Techniques." RMIT University. Electrical and Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20061222.113303.

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This thesis aims to contribute to the developments of wireless communication systems. The work generally consists of three parts: the first part is a discussion on general digital communication systems, the second part focuses on wireless channel modelling and fading mitigation techniques, and in the third part we discuss the possible application of advanced digital signal processing, especially time-frequency representation and blind source separation, to wireless communication systems. The first part considers general digital communication systems which will be incorporated in later parts. Today's wireless communication system is a subbranch of a general digital communication system that employs various techniques of A/D (Analog to Digital) conversion, source coding, error correction, coding, modulation, and synchronization, signal detection in noise, channel estimation, and equalization. We study and develop the digital communication algorithms to enhance the performance of wireless communication systems. In the Second Part we focus on wireless channel modelling and fading mitigation techniques. A modified Jakes' method is developed for Rayleigh fading channels. We investigate the level-crossing rate (LCR), the average duration of fades (ADF), the probability density function (PDF), the cumulative distribution function (CDF) and the autocorrelation functions (ACF) of this model. The simulated results are verified against the analytical Clarke's channel model. We also construct frequency-selective geometrical-based hyperbolically distributed scatterers (GBHDS) for a macro-cell mobile environment with the proper statistical characteristics. The modified Clarke's model and the GBHDS model may be readily expanded to a MIMO channel model thus we study the MIMO fading channel, specifically we model the MIMO channel in the angular domain. A detailed analysis of Gauss-Markov approximation of the fading channel is also given. Two fading mitigation techniques are investigated: Orthogonal Frequency Division Multiplexing (OFDM) and spatial diversity. In the Third Part, we devote ourselves to the exciting fields of Time-Frequency Analysis and Blind Source Separation and investigate the application of these powerful Digital Signal Processing (DSP) tools to improve the performance of wireless communication systems.

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Książki na temat "Autocorrelation function (ACF)"

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

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The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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Części książek na temat "Autocorrelation function (ACF)"

Singh, Sudip. "Predicting Daily Confirmed COVID-19 Cases in India." In Advances in Medical Technologies and Clinical Practice, 34–51. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7188-0.ch003.

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India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

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Ando, Yoichi. "Magnetoencephalographic Evoked Responses to Factors Extracted from the Autocorrelation Function (ACF)/the Inter-Aural Cross-Correlation Function (IACF) Factors." In Signal Processing in Auditory Neuroscience, 35–38. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-815938-5.00005-4.

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Ross, John, Igor Schreiber, and Marcel O. Vlad. "Experimental Test and Applications of Correlation Metric Construction." In Determination of Complex Reaction Mechanisms. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195178685.003.0010.

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In this chapter we present an experimental test case of the deduction of a reaction pathway and mechanism by means of correlation metric construction from time-series measurements of the concentrations of chemical species. We choose as the system an enzymatic reaction network, the initial steps of glycolysis. Glycolysis is central in intermediary metabolism and has a high degree of regulation. The reaction pathway has been well studied and thus it is a good test for the theory. Further, the reaction mechanism of this part of glycolysis has been modeled extensively. The quantity and precision of the measurements reported here are sufficient to determine the matrix of correlation functions and, from this, a reaction pathway that is qualitatively consistent with the reaction mechanism established previously. The existence of unmeasured species did not compromise the analysis. The quantity and precision of the data were not excessive, and thus we expect the method to be generally applicable. This CMC experiment was carried out in a continuous-flow stirred-tank reactor (CSTR). The reaction network considered consists of eight enzymes, which catalyze the conversion of glucose into dihydroxyacetone phosphate and glyceraldehyde phosphate. The enzymes were confined to the reactor by an ultrafiltration membrane at the top of the reactor. The membrane was permeable to all low molecular weight species. The inputs are (1) a reaction buffer, which provides starting material for the reaction network to process, maintains pH and pMg, and contains any other species that act as constant constraints on the system dynamics, and (2) a set of “control species” (at least one), whose input concentrations are changed randomly every sampling period over the course of the experiment. The sampling period is chosen such that the system almost, but not quite, relaxes to a chosen nonequilibrium steady state. The system is kept near enough to its steady state to minimize trending (caused by the relaxation) in the time series, but far enough from the steady state that the time-lagged autocorrelation functions for each species decay to zero over three to five sampling periods. This long decay is necessary if temporal ordering in the network is to be analyzed.

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Streszczenia konferencji na temat "Autocorrelation function (ACF)"

Thayoob, Yasmin H. Md, P. S. Ghosh, and Ahmad Basri Abd Ghani. "Preprocessing of Electrical Partial Discharge Signals Using Autocorrelation Function (ACF)." In 2006 IEEE International Power and Energy Conference. IEEE, 2006. http://dx.doi.org/10.1109/pecon.2006.346721.

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Zvyagin, Petr. "On Autocorrelation of Ice Loads, Measured in Ice Tank Experiments." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54436.

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Temporal dynamics of ice loads, which are measured with high sampling rate in experiments in ice tank, usually show strong autocorrelation. At the moment it is lack of study on autocorrelation function (ACF) of ice loads time series. In this paper the stochastic processes approach will be applied to analyze autocorrelation of ice loads. Stochastic models for ice loads, developed earlier by the author, allow determining the distribution law and stationarity (in wide sense) for some of such time series in statistically confident manner. That allows conducting further study of those time series ACF. For analysis and correct sampling rate choice it is important to know the time interval, which separates two statistically independent data points in time series. The algorithm for finding of such interval for time series with normal and lognormal distribution was developed in the paper. That algorithm was applied to find independence distance for global loads records, obtained in experiments with cylindrical models in ice tank of Krylov State Research Centre (St. Petersburg). The independence distance for those time series occurred to be 0,07–0,35 sec. That distance had increased with increasing of indenter diameter. Obtained results are discussed.

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Urzică, Ana C., Mihaela Rodica D. Bălan, and Spiridon S. Creţu. "Pressures Distributions and Depth Stresses Developed in Concentrated Contacts Between Elements With Non-Gaussian Rough Surfaces." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82357.

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The main step in a contact analysis between rough surfaces is the evaluation of the pressure distribution developed on the real contact area. The normal pressure distribution can be used further to predict subsurface stress, interfacial friction and surface wear. Only few real rough surfaces are Gaussian, most of that produced by manufacturing processes are non-Gaussian having an arbitrarily orientation. The definition of the roughness will include, in addition to the spatial characteristics, information about spectral characteristics of the surface, described with the use of autocorrelation function ACF. Conjugate Gradient Method in conjunction with Discrete Convolution Fast Fourier Transform was refined and applied to solve the constrained system of equations that governs the pressure distribution between rough surfaces subjected to contact loading in elastic dry conditions. The paper reveals the role played by the spatial characteristics (standard deviation σ, skewness Sk, and kurtosis K) as well as by autocorrelation function on both the non-Hertzian pressure distribution on contact area and depth distribution of von Mises equivalent stress.

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Hariri, A., J. W. Zu, and R. Ben Mrad. "A New Multiple Point Asperity Model for Contact Between Nominally Flat Surfaces." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13720.

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For several decades, asperities of nominally flat rough surfaces were considered to be points higher than their immediate neighbors. Recently, it has been recognized that this model is inadequate. To address the issue, a new multiple point asperity model, called η-point asperity model, is introduced in this paper. In the new model, asperities are composed of η neighboring sampled points with η-2 middle points being above a certain level. When the separation between two surfaces decreases, new asperities with higher number of sample points, η, will come into existence. Based on the above model, the height and curvature of η-point asperities are defined and their distributions are found. The model is developed for Gaussian surfaces and for the general case of autocorrelation function (ACF). As a case study, the exponential ACF is applied to the new model, which is shown to produce remarkably good agreement with measurements from real and simulated surfaces.

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Hariri, A., J. W. Zu, and R. Ben Mrad. "Modeling of the Elastic Properties of Contact Between Nominally Flat Rough Surfaces Using a New Multiple Point Asperity Model." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14130.

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The asperities of rough surfaces have long been considered to be points higher than their immediate neighbors. Based on this concept, theories were developed for quantitatively understanding the nature of contact between rough surfaces. Recently it has been recognized that the above model for asperities is inadequate. Consequently, all the models that have been developed based on that model are inadequate as well. In this paper, based on a newly developed multiple-point asperity model, the elastic contact problem between nominally flat surfaces is reformulated. This leads to finding the deformed area, and load produced by the contact. The model is developed for the general form of isotropic rough surfaces with arbitrary height distribution and autocorrelation function (ACF). The microcontact areas generated by each asperity contact are considered to be circles. The Gaussian distribution of heights and exponential ACF are considered as a benchmark to compare the results of the new model with the existing models. Using results from numerical models developed by other groups, the new model is validated.

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Neumann, Karoline M., and Sören Ehlers. "Power Spectrum for Surface Description of Corroded Ship Structure From Laser Scan." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95907.

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Abstract Strength of corroded marine structures is in the industry traditionally assessed using average thickness reduction together with the assumption of a uniform surface. In academia it is however realised that the surface texture will influence the strength where the effect of the irregular surface is considered through various parameters such as reduced material properties, roughness, geometrical ratios and thickness distributions. In this paper, the objective is to present a power spectrum which describes the corroded surface topography. Therefore, power spectra of surface and thickness are produced from 3D laser scan of real corroded mild steel specimens from a 30 year old ship using Fast Fourier Transform (FFT). The frequency distribution describes the spatial variability, wherefrom the autocorrelation function (ACF) is deduced. A log-log fit is made to the power spectrum, where the interception with the ordinate axis describes the corrosion severity. This way of describing the surface pattern of the corrosion deterioration may serve as input to novel ways of calculating corroded strength and fatigue initiation as well as simulation of corroded surfaces.

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Yang, Guoqing, Jun Hong, Linbo Zhu, Shaofeng Wang, Meihua Xiong, and Guoqing Yang. "Finite Element Modeling of the Elastic-Plastic Contact Between Two Rough Surfaces." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86522.

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In order to investigate the complex contact behaviors of micro-rough interfaces, this paper proposed a novel finite element method (FEM) for simulating the contact between two rough surfaces. The numerical methods for generating Gaussian and non-Gaussian rough surfaces were introduced, and the contact surfaces were produced with given autocorrelation function (ACF) and the first four moments of surface height. Finite element (FE) models were constructed based on the generated rough surfaces, in which 3D structural modeling and meshing strategies were utilized to improve the mesh quality and solution efficiency. A 3D finite element analysis (FEA) was conducted to investigate the contact characteristics including the contact stiffness, real contact area and the contact pressure distribution of rough interfaces. The results of the contacts between two rough surfaces were compared with those of the conventional equivalent contact models in which an equivalent rough surface contacted with a rigid smooth plane. The proposed FEM makes it effective to predicate or evaluate the contact characteristics of the contact of two rough surfaces, and may provide a new way to optimize the design of surface topographies and improve the performances of contact interfaces.

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Jonas, M. "The Application of the Time Series Theory to Processing Data From the SBAS Receiver in Safety Mode." In 2012 Joint Rail Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/jrc2012-74033.

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Before satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) in the USA, and the European Geostationary Navigation Overlay Service (EGNOS), will be used in railway safety-related applications, it is necessary to determine reliability attributes of these systems as quality measures from the user’s point of view. It is necessary to find new methods of processing data from the SBAS system in accordance with strict railway standards. For this purposes data from the SBAS receiver with the Safety of Life Service was processed by means of the time series theory. At first, a basic statistic exploration analysis by means of histograms and boxplot graphs was done. Then correlation analysis by autocorrelation (ACF), and partial autocorrelation functions (PACF), was done. Statistical tests for the confirmation of non-stationarity, and conditional heteroscedasticity of time series were done. Engle’s ARCH test confirmed that conditional heteroscedasticity is contained. ARMA/GARCH models were constructed, and their residuals were analyzed. Autocorrelation functions and statistical tests of models residuals were done. The analysis implies that the models well cover the variance volatility of investigated time series and so it is possible to use the ARMA/GARCH models for the modeling of SBAS receiver outputs.

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