ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (21949042)10(4/W1-2022)pp. 631-637
Northwest of Iran, as a tectonically active region, has experienced numerous devastating earthquakes. That is why it is so important to study the earth deformation in this area and to provide more precise insights. So far, most researchers have had the preference of using the invariant component of strain rate tensor for investigating the Earth's shape deformation in the region. However, to examine the efficiency of the variant components of the geodesic strain rate tensor in interpreting deformations of north-western Iran, we have in this article maps of variant components of the geodetic strain rate tensor (normal strain rate along north and eastbound). Using the velocity field gathered from a previous article, and also using a simple and straightforward method, the strain rate tensors were calculated. The obtained contraction along the north direction (from the normal strain along this axis) confirms the Eurasia-Arabia collision. Besides, the obtained extension along the east direction and the derived expansion of the dilatation, show the effect of Anatolian motion to the west and eastward movement of the central Iran plateau on the tectonic structure of the studied area. These two results showed that the variant component of strain rate tensor also provides us with useful information about a region shape deformation. © Author(s) 2023. CC BY 4.0 License.
Natural Resources Research (15207439)32(3)pp. 1007-1020
Improper abstraction of groundwater in Iran has led to an average annual subsidence rate of 15 cm/yr. The management of Iran's water resources is essential due to its arid and semiarid climate and traditional agriculture. Monitoring groundwater storage (GWS) changes and their correct interpretation using deep learning (DL) methods can improve our understanding of groundwater systems. For this purpose, in this study, the GWS in Iran from 2003 to 2021 was downscaled using DL based on combining gravity recovery and climate experiment (GRACE) and GRACE-Follow on with a hydrological model. The GWS downscaling was performed from 1° to 0.25°. The GWS in the south of Tehran and northeast of Qazvin had the highest decrease of 15 mm/yr. A new GWS index was developed to correctly interpret the decline in GWS through the standardized precipitation index. The main reason for the decrease in GWS was the development of unsustainable agriculture from 2007 to 2012, which reached its lowest possible level after 2012–2018 with the intensification of climatic conditions. The calculated GWS index correlates more than 80% with 400 piezometric wells in Iran. © 2023, International Association for Mathematical Geosciences.
Journal of Hydrology: Regional Studies (22145818)50
Study region: The Kabudarahang Plain and the Razan-Qahavand Plain. Study focus: Improper use of water resources has reduced groundwater levels and created land subsidence (LS) in many plains of Iran. The aim and innovation of this research are to study multi-sensor observations for LS and groundwater depletion and explore the relationships of the involved variables with high confidence. The gravity recovery and climate experiment (GRACE) observations can be used to evaluate water storage changes at the Earth's surface. GRACE has stripe errors, leakage and various noises that multilevel 3D wavelet decomposition (M3WD) has been suggested to mitigate noises and downscale for small scale. This study has investigated the interferometric synthetic-aperture radar (InSAR) of Sentinel-1 images from October 2014 to September 2019, the GRACE data from March 2002 to July 2016, and groundwater hydrograph (GH) from 2014 to 2020. New hydrological insight for the region: The maximum LS rate, obtained from small baseline subset-differential of InSAR is 20 mm/year at the Kabudarahang Plain (KP) and 30 mm/year at Razan-Qahavand Plain (RQP). The groundwater storage variations (ΔGW) have a decreasing trend of 78.45 ± 0.2 million cubic meters/year. The GH for the KP and RQP shows a downward trend of 3.25 and 1.81 m/year, respectively. Based on the outcomes, the M3WD can increase the correlation of ΔGW with other sensors by 15 %. Also, validation between sensors with normalized cross-correlation has remarkable compatibility. The multi-sensor study of ΔGW and LS revealed various dimensions with high reliability and can facilitate the water resource management. © 2023 The Authors
Meteorological Applications (13504827)30(6)
The weighted mean temperature ((Formula presented.)) plays a crucial role in calculating Precipitable Water Vapor (PWV) and integrated water vapor (IWV) using Global Navigation Satellite Systems (GNSS) techniques. Currently, the primary sources for meteorological parameters are radiosonde measurements and Numerical Weather Models (NWMs). This study focuses on assessing the influence of different data sources on the computation of (Formula presented.) and IWV in Iran. The investigation involved comparing several datasets: ERA5 numerical data with spatial resolutions of 0.125° and 2.5° (ERA5 0.125, ERA5 2.5), ERA-Interim, NCEP numerical data and (Formula presented.) results derived from the GPT3 model. Validation of the results utilized data from 12 radiosonde stations situated across Iran. In addition, the precision of the IWV parameter was evaluated by utilizing measurements from the only available IGS station in the region, situated in Tehran. The results revealed that ERA5 0.125 exhibited superior accuracy in (Formula presented.) estimation compared with the other datasets, showing a discrepancy of approximately 1–2 K. In contrast, the GPT3 model displayed an accuracy of about 3 K. Analysing the results across different months of the year revealed elevated root mean square error (RMSE) values during warmer months, with little variability based on station height in the region for the four datasets. Regarding IWV, the ERA5 0.125 dataset outperformed the other three datasets, demonstrating an accuracy of about 0.07 kg m−2. Notably, RMSE values during summer were approximately 50% higher compared with the annual RMSE. © 2023 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
Abbasi, M.,
Ghods, A.,
Najafi, M.,
Abbasy, S.,
Amiri, M.,
Shabanian, E.,
Kheradmandi, M.,
Asgari, J. Tectonophysics (00401951)869
The Makran Subduction Zone is segmented into western and eastern Makran where all instrumentally recorded megathrust earthquakes happened in eastern Makran. To find out how the lack of megathrust earthquakes and the observed low seismicity rate in western Makran is related to a combination of long interseismic quiescence, aseismic creep, or low rate of interseismic strain accumulation within the megathrust zone, we have complemented the five existing GPS vectors along the coasts of Makran by nine new GPS vectors and an extra eight new GPS vectors across the width of the Makran megathrust zone. Our block modeling shows that the coupling of the subducting Arabian oceanic plate with the overriding plate in western Makran is more than four times smaller than that of eastern Makran. Additionally, the maximum interseismic strain rate accumulation within the onshore part of the megathrust zone of western Makran is >7 times smaller than that in eastern Makran. We found a right-lateral motion of about ∼16 mm across the transfer zone between Zagros and Makran. We consider a much lower earthquake hazard for western Makran relative to that of eastern Makran because the overriding Lut block is moving northward causing much less strain accumulation within the megathrust zone, and because of the lower seismic coupling between the subducting and overriding plates. Our findings show a large strain rate across the transfer zone between Zagros and Makran and thus imply a much higher earthquake hazard for the transfer zone. © 2023
Survey Review (00396265)54(385)pp. 349-362
The NUVEL1-A is one of the old and popular plate tectonic models. While the NUVEL1-A is a geological-based model, recently a model has been proposed (GSRM2.1 model) which is based on the results of space geodetic techniques. In this work, we investigate the consistency of these models with the VLBI and SLR results in Europe. Direction and magnitude of the horizontal motion from NUVEL-1A and GSRM2.1 models are compared with corresponding values from both geodetic techniques. This comparison provides valuable deductions such as: (1) The values of geodetic-based model (GSRM2.1) show better agreement with SLR and VLBI results (2) In each comparison between geodetic results and modelled values, direction divergence is larger than magnitude difference. © 2021 Survey Review Ltd.
Mirmohammadian, F.,
Asgari, J.,
Verhagen, S.,
Amiri-simkooei, A. Remote Sensing (20724292)14(1)
With the advancement of multi-constellation and multi-frequency global navigation satellite systems (GNSSs), more observations are available for high precision positioning applications. Although there is a lot of progress in the GNSS world, achieving realistic precision of the solution (neither too optimistic nor too pessimistic) is still an open problem. Weighting among different GNSS systems requires a realistic stochastic model for all observations to achieve the best linear unbiased estimation (BLUE) of unknown parameters in multi-GNSS data processing mode. In addition, the correct integer ambiguity resolution (IAR) becomes crucial in shortening the Time-To-Fix (TTF) in RTK, especially in challenging environmental conditions. In general, it is required to estimate various variances for observation types, consider the correlation between different observables, and compensate for the satellite elevation dependence of the observable precision. Quality control of GNSS signals, such as GPS, GLONASS, Galileo, and BeiDou can be performed by processing a zero or short baseline double difference pseudorange and carrier phase observations using the least-squares variance component estimation (LS-VCE). The efficacy of this method is investigated using real multi-GNSS data sets collected by the Trimble NETR9, SEPT POLARX5, and LEICA GR30 receivers. The results show that the standard deviation of observations depends on the system and the observable type in which a particular receiver could have the best performance. We also note that the estimated variances and correlations among different observations are also dependent on the receiver type. It is because the approaches utilized for the recovery techniques differ from one type of receiver to another kind. The reliability of IAR will improve if a realistic stochastic model is applied in single or multi-GNSS data processing. According to the results, for the data sets considered, a realistic stochastic model can increase the computed empirical success rate to 100% in multi-GNSS as well as a single system. As mentioned previously, the realistic precision of the solution can be achieved with a realistic stochastic model. However, using the estimated stochastic model, in fact, leads to better precision and accuracy for the estimated baseline components, up to 39% in multi-GNSS. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Acta Geophysica (18956572)70(3)pp. 1445-1454
Weighted mean temperature (Tm) is used to determine water vapor content, precipitable water vapor, and integrated water vapor (IWV) in GNSS. This parameter is highly correlated with climate conditions as well as the type of the region. The case study is performed in Iran which has diverse climate. ERA5 reanalysis datasets were used at a compact grid of 0.125 × 0.125 between 2007 and the end of 2019 to model the Tm. The data obtained from 12 radiosonde stations along with an IGS station located in Tehran were employed in this research. Five models were examined for Tm. Bevis model, linear grouping model (LGM), and linear nearest grid point model (LNGPM) were considered as Tm linear models, and harmonic model (HM) and GPT2w model were used as nonlinear models. In LGM method the study region was divided into smaller areas with different linear model coefficients using spatial grouping method. The local model in each radiosonde station was considered as a reference. According to the results, the accuracy of linear models (Bevis and LGM model) was between 3 and 8 K (radiosonde data as reference); also 7 out of 12 stations in the LGM had higher accuracy than the Bevis model (based on RMSE). The accuracy of the two GPT2w models and the harmonic model was higher than the previous two models, and it was between 2 and 4 K. The IWV values were obtained using zenith total delay observations of IGS station located in Tehran using 5 models and were compared with the IWV values of the radiosonde station. The accuracy of the values in three linear models, Bevis, LGM, and LNGPM, was, respectively, 0.2, 0.17, and 0.14 kg m−2, and in the two nonlinear models, GPT2w and HM, was 0.13 kg m−2. © 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.
Mirmohammadian, F.,
Asgari, J.,
Verhagen, S.,
Amiri-simkooei, A. Sensors (14248220)22(15)
Until now, RTK (real-time kinematic) and NRTK (Network-based RTK) have been the most popular cm-level accurate positioning approaches based on Global Navigation Satellite System (GNSS) signals in real-time. The tropospheric delay is a major source of RTK errors, especially for medium and long baselines. This source of error is difficult to quantify due to its reliance on highly variable atmospheric humidity. In this paper, we use the NRTK approach to estimate double-differenced zenith tropospheric delays alongside ambiguities and positions based on a complete set of multi-GNSS data in a sample 6-station network in Europe. The ZTD files published by IGS were used to validate the estimated ZTDs. The results confirmed a good agreement, with an average Root Mean Squares Error (RMSE) of about 12 mm. Although multiplying the unknowns makes the mathematical model less reliable in correctly fixing integer ambiguities, adding a priori interpolated ZTD as quasi-observations can improve positioning accuracy and Integer Ambiguity Resolution (IAR) performance. In this work, weighted least-squares (WLS) were performed using the interpolation of ZTD values of near reference stations of the IGS network. When using a well-known Kriging interpolation, the weights depend on the semivariogram, and a higher network density is required to obtain the correct covariance function. Hence, we used a simple interpolation strategy, which minimized the impact of altitude variability within the network. Compared to standard RTK where ZTD is assumed to be unknown, this technique improves the positioning accuracy by about 50%. It also increased the success rate for IAR by nearly 1. © 2022 by the authors.
International Journal of Remote Sensing (01431161)43(14)pp. 5173-5203
Accurate knowledge of soil moisture is critical for hydrological and agricultural applications such as agricultural irrigation management, drought characterization, and flood detection. Researchers have attempted to provide soil moisture using various methods and techniques. Traditionally, the amount of soil moisture was based on field measurements. On the other hand, remote sensing satellites have been widely used to provide continuous soil moisture measurements worldwide, encountering problems such as the lack of simultaneous spatial and temporal sampling rates and dependence on weather conditions. However, in recent decades, GNSS signals reflected from the Earth’s surface (GNSS-R technique) have been increasingly used for soil moisture monitoring, due to the numerous advantages it offers. This paper aims to provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSS-R observations. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Marine Georesources and Geotechnology (1064119X)40(3)pp. 361-369
Lake Urmia is located in the northwest of Iran and shared between the provinces of West Azarbaijan and East Azarbaijan. In the last two decades, there has been a considerable decline in the lake’s water level. Satellite altimetry (SA) together with the advanced precise orbital positioning system has reached a high accuracy in the measurement of the water level height, but increasing the accuracy of waveform retracking (WR) is a challenging issue. In this study, wavelet decomposition and convolutional neural network were used for the WR with 50%, 55%, and 60% training scenarios and the threshold method was used for the 1992–2019 period. The training of 55% has the best result with a ± 0.027 m root mean square error. The water level has decreased by approximately 7 m from 1994 to 2018 and its overall trend is downward. The proposed method has been able to increase the WR accuracy by up to 30%. The gravity recovery and climate experiment and the annual monitoring of the water level station have also been used for the SA verification, which have a significant correlation of 0.66 and 0.96 with SA, respectively. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Remote Sensing Letters (2150704X)12(5)pp. 499-509
Monitoring the melting of Greenland ice using various sensors is of great importance due to global sea level rise. The mass changes in Greenland can be observed with the GRACE (Gravity Recovery and Climate Experiment) mission from 2002 to 2016. The GRACE limitations and noise are due to the geometrical and instrumental properties along its orbit, which requires investigations for further improvement. The innovation of this research is to introduce a new method in four-dimensional (4D) wavelet decomposition (WD) for increasing the efficiency of the GRACE signal, used for the reconstruction of the Greenland mass changes. The results show that the overall downward trend in the west Greenland coast is 25.25 ± 6.95 cm/year, and the highest decline rate is 33.60 ± 6.23 cm/year from 2013 to 2016. The northern regions of Greenland have less mass loss than the west and south. For verification, the 4D WD output has been compared with the CryoSat-2 results from 2011 to 2016. The GRACE and CryoSat-2 show a significant correlation of 0.62, which indicates an improvement of 0.18 compared to the forward modelling. The 4D WD improves the overall performance of the reconstructed signal in the frequency time-space domain and reduces the noise in each dimension. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Measurement: Journal of the International Measurement Confederation (02632241)174
The gravity recovery and climate experiment (GRACE) satellites detect changes in the distribution of water on the Earth surface based on the gravity anomaly. A correct reconstruction of the GRACE signal is still a challenging problem due to signal attenuation and noise levels. In this contribution, the GRACE data are analysed using a deep neural network to investigate changes caused in the Caspian Sea level (CSL). The novelty is to reconstruct the three-dimensional GRACE signal for reducing stripe errors, leakage error and gap filling. The reduction of the CSL was approximately 70 ± 0.2 cm from 2005 to 2016, with an annual trend of −6.77 ± 0.2 cm/year. The northern regions of CSL have a smaller annual amplitude than other regions. The proposed method has an average significant correlation of 82% with satellite altimetry and data collected by three tide gauge stations, thus showing good compatibility. © 2021 Elsevier Ltd
Amiri-simkooei, A.,
Hosseini-asl m., ,
Asgari, J.,
Zangeneh-nejad f., F. GPS Solutions (10805370)23(1)
Proper analysis and subsequent interpretation of GPS position time series is an important issue in many geodetic and geophysical applications. The GPS position time series can possibly be contaminated by some abrupt changes, called offsets, which can be well compensated for in the functional model. An appropriate offset detection method requires proper specification of both functional and stochastic models of the series. Ignoring colored noise will degrade the performance of the offset detection algorithm. We first introduce the univariate analysis to identify possible offsets in a single time series. To enhance the detection ability, we then introduce the multivariate analysis, which considers the three coordinate components, north, east and up, simultaneously. To test the performance of the proposed algorithm, we use synthetic daily time series of three coordinate components emulating real GPS time series. They consist of a linear trend, seasonal periodic signals, offsets and white plus colored noise. The average detection power on individual components, either north, east or up, are 32.3 and 47.2% for the cases of white noise only and white plus flicker noise, respectively. The detection power of the multivariate analysis increases to 70.8 and 87.1% for the above two cases. This indicates that ignoring flicker noise, existing in the structure of the time series, leads to lower offset detection performance. It also indicates that the multivariate analysis is more efficient than the univariate analysis for offset detection in the sense that the three coordinate component time series are simultaneously used in the offset detection procedure. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Zangeneh-nejad f., F.,
Amiri-simkooei, A.,
Sharifi m.a., ,
Asgari, J. Journal of Surveying Engineering (07339453)144(4)
There are two different but equivalent forms to solve a least squares (LS) adjustment problem, namely batch LS (BLS) and recursive LS (RLS). The batch form is usually not an appropriate approach when measurements are sequentially received over time. When the unknown parameters are constant and hence converge slowly over time to their final estimates, the RLS algorithm can be used. For time-varying parameters, the Kalman filter (KF) algorithm can be used. To properly reflect the time variations of the parameters, this method requires the appropriate choice of filter parameters, and hence tuning is an important stage. In some geodetic applications, as time progresses, new observations and parameters are added to the system of equations. For such applications, an RLS algorithm with additive parameters can be developed. The method differs from the KF in the sense that there is no need to define and manipulate the dynamic model and the noise structure of the parameters involved. The implementation of the method in both linear and nonlinear models is explained. As an application of the proposed method, the algorithm is implemented in the global positioning system (GPS) precise point positioning (PPP), either in static or kinematic mode. Generally, the PPP adjustment is performed via a sequential filter: either the LS sequential filter, or the discrete KF. We propose an efficient alternative based on the RLS with additive parameters, which is applicable to PPP. The performance of the method is investigated with regard to the repeatability and accuracy using a few 24-h data sets of four International GNSS Service (IGS) stations. Finally, the efficacy of the proposed method is investigated in the kinematic mode using two experiments: a stationary experiment and a real kinematic test. The results indicate that the proposed method can be employed as an appropriate alternative to the KF algorithm. © 2018 American Society of Civil Engineers.
Khazraei, S.M.,
Nafisi, V.,
Amiri-simkooei, A.,
Asgari, J. Journal of Surveying Engineering (07339453)143(2)
In the last few years, satellite-based positioning techniques, such as the Global Positioning System (GPS), proved their abilities in various geodetic fields. Height determination, as one of such applications, requires a more likely correct geoid model to provide reliable geoid heights for transformation of the ellipsoidal heights to orthometric heights. An important step is then to establish such a model by optimal combination of the available geoid models. This can be achieved via variance component estimation (VCE) methods, which provide appropriate weights to GPS and leveling observations as well as the geoid models. The authors demonstrate the efficacy of the least-squares VCE (LS-VCE) to this problem. The algorithm is applied to real data sets in Shahin-Shahr, Isfahan, Iran, to evaluate the EGM2008 and GGMplus Earth geopotential models and a regional geoid model (named IRGeoid10) in terms of agreement to the GPS/leveling observations and introduce the more likely correct model over the case-study area. The results indicate that the EGM2008 model shows a good agreement (2-mm precision on the fitted surface) with the results of the GPS/leveling observations in this very small area. It is notable, however, that the present contribution is mainly of interest from an algorithmic point of view because concrete conclusions cannot be made when comparing the results due to the small case-study area used. By optimal combination of these data sets, the weights of which are estimated by LS-VCE, a geometric surface is presented to approximate the local vertical datum in the case-study region. This surface can convert the ellipsoidal heights, which can be obtained from GPS, to the orthometric heights with high precision. © 2016 American Society of Civil Engineers.
Meteorological Applications (13504827)24(4)pp. 642-642
The article by S. Abbasy et al. contained an error in affiliation of author Jamal Asgari. His correct affiliation should be as follows: Department of Geomatics Engineering, University of Isfahan, Iran The authors apologize for any confusion or inconvenience caused by this error. © 2017 Royal Meteorological Society
Zangeneh-nejad f., F.,
Amiri-simkooei, A.,
Sharifi m.a., ,
Asgari, J. GPS Solutions (10805370)21(4)pp. 1593-1603
Cycle slip detection and repair is an important issue in the GPS data processing. Different methods have been developed to detect and repair cycle slips on undifferenced , single- or double-differenced observations. The issue is still crucial for high-precision GPS positioning, especially for the undifferenced GPS observations. A method is proposed to fix cycle slips based on the generalized likelihood ratio (GLR) test. The method has a good performance on cycle slip fixing of undifferenced carrier phase observations on individual frequencies, either on L1 or on L2, without making a linear combination among the observables. The functional model is a piecewise cubic curve fitted to a number of consecutive data using the least squares cubic spline approximation (LS-CSA). For fixing the cycle slips, an integer estimation technique is employed to determine the integer values from the float solution. The performance of the proposed method is then compared with the existing two methods using simulated data. The results on a few GPS data sets with sampling rate of 1 Hz or higher confirm that this method can detect and correct all simulated cycle slips regardless of the size of the cycle slip or the satellite elevation angle. The efficacy of the method is then investigated on the GPS data sets with lower sampling rates of 5, 10, and 30 s. The results indicate that the proposed method always performs the best for the data sets considered. This is thus an appropriate method for cycle slip detection and repair of single-frequency GPS observations. © 2017, Springer-Verlag Berlin Heidelberg.
Journal of the Earth and Space Physics (2538371X)43(1)pp. 165-180
Tidal observations have been widely used for a variety of applications. Realistic functional and stochastic models of tidal observation are then required. The functional model is complete if one knows the tide characteristics such as tidal frequencies (M2 and S2 for instance). The stochastic model is complete if we know noise characteristics of tidal observations. There is always a prediction error between the predicted values and the observed tide heights. This can be investigated when taking the noise characteristics of tidal time series observations. Functional model identification is however the subject of discussion in the present contribution. Tidal data are frequently used for different applications such as safe navigation. Real tidal gauge data can be expressed by their tidal constituents (frequencies) and a noise structure. Using tidal frequencies and tidal observations one can employ the functional model to predict tide. Therefore identifying tidal frequencies is an important issue for tidal analysis. So far, most of the available methods to determine tidal frequencies have been based on theory, and sea level height observations have not seriously been used to extract tidal frequencies. The theory-based methods usually apply the ephemeris of Moon, Sun and other planets to extract tidal frequencies without the use of tidal observations. Following-up the study by Amiri-Simkooei et al. (2014), we further focus on extracting tidal frequencies using tidal observations. For this purpose, we apply the least squares harmonic estimation (LS-HE) to the multivariate tidal time series. As a generalization of the Fourier spectral analysis, LS-HE is neither limited to evenly spaced data nor to integer frequencies. We may also note that the main tidal constituents may change from one area to another area. In this contribution, we use the data sets of eight coastal tide gauge stations in the Persian Gulf and Oman Sea between 1999 and 2010 with a sampling rate of 30 min using a multivariate analysis. In multivariate analysis, the frequencies contributed in tide structure are more obvious than in the univariate analysis. Such signals can thus simply be detected in the multivariate analysis. Using the above-mentioned data, 414 main tidal constituents have been extracted. Our extracted lists of frequencies (of the Persian Gulf and Oman Sea) are compared with the two lists of frequencies consisting of 50 and 121 frequencies by the study of Amiri-Simkooei et al. (2014), which was applied to UK tide gauge stations. In the present contribution, new frequencies that belong to the tide gauge stations of the Persian Gulf and Oman Sea have been identified. Finally, a six-month prediction is performed for all stations using the two lists of main frequencies obtained in the two studies. The prediction results of the two studies are then compared using the estimated root mean squared error (RMSE). The RMSE difference of our predicted data show a reduction ranging from 2 cm to 7 cm compared to that predicted using the frequency lists of Amiri-Simkooei et al. (2014). The estimated RMSE of tide prediction using the frequencies obtained in this study ranges from 9 to 16 cm.
Meteorological Applications (13504827)24(3)pp. 415-422
Meteorological investigations using the global positioning system (GPS) are based on expensive permanent networks and they are not developed globally on the Earth. In this study it is confirmed that single station GPS meteorology is feasible where there is no possibility for development of a sophisticated dense GPS network. Since 1 January 2011 a GPS station has been installed in the Institute for Advanced Studies in Basic Sciences in the province of Zanjan, Iran, where upper air meteorological data are not available. The GPS data were processed in order to estimate the zenith total delay (ZTD) of GPS signals due to the troposphere. The estimated ZTD was then transformed to precipitable water vapour (PWV) using the ERA-Interim globally available humidity and temperature vertical profiles. Three kinds of validation were applied to the estimated PWV and all of them reasonably proved the validity of the GPS results: (1) the measured surface water vapour pressure and dew point temperature show 87.8 and 86.6% correlation respectively with the estimated PWV; (2) the PWV measured using radiosondes in three neighbouring cities of Zanjan (Tabriz, Tehran and Kermanshah) with nearly the same climatic regime show 81.1, 71.7 and 66.4% correlation respectively with the GPS PWV time series, and (3) the global reanalysis datasets for Zanjan show 89.2% correlation with the GPS results. These validations indicate that, in the absence of permanent GPS networks, if proper data processing strategies are adopted the low cost single station GPS meteorology can be considered as a possibility for meteorological monitoring. © 2017 Royal Meteorological Society
Zangeneh-nejad f., F.,
Amiri-simkooei, A.,
Sharifi m.a., ,
Asgari, J. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (16821750)42(4W4)pp. 531-536
Geodetic data processing is usually performed by the least squares (LS) adjustment method. There are two different forms for the LS adjustment, namely the batch form and recursive form. The former is not an appropriate method for real time applications in which new observations are added to the system over time. For such cases, the recursive solution is more suitable than the batch form. The LS method is also implemented in GPS data processing via two different forms. The mathematical model including both functional and stochastic models should be properly defined for both forms of the LS method. Proper choice of the stochastic model plays an important role to achieve high-precision GPS positioning. The noise characteristics of the GPS observables have been already investigated using the least squares variance component estimation (LS-VCE) in a batch form by the authors. In this contributioi we introduce a recursive procedure that provides a proper stochastic modeling for the GPS observables using the LS-VCE. It is referred to as the recursive LS-VCE (RLS-VCE) method, which is applied to the geometry-based observation model (GBOM). In this method, the (co)variances parameters can be estimated recursively when the new group of observations is added. Therefore, it can easily be implemented in real time GPS data processing. The efficacy of the method is evaluated using a real GPS data set collected by the Trimble R7 receiver over a zero baseline. The results show that the proposed method has an appropriate performance so that the estimated (co)variance parameters of the GPS observables are consistent with the batch estimates. However, using the RLS-VCE method, one can estimate the (co)variance parameters of the GPS observables when a new observation group is added. This method can thus be introduced as a reliable method for application to the real time GPS data processing.
GPS Solutions (10805370)20(1)pp. 89-100
The cost of inertial navigation systems (INS) has decreased significantly during recent years using micro-electro-mechanical system technology in production of inertial measurement units (IMUs). However, these IMUs do not provide the accuracy and stability of their classical mechanical counterparts which limit their applications. Hence, the error control of such systems is of the great importance which is achievable using external information via an appropriate fusion algorithm. Traditionally, this external information can be derived from global positioning system (GPS). But it is well known that GPS data availability and accuracy are vulnerable to signal-degrading circumstances and satellite visibility. We introduce a standalone attitude and heading reference system (AHRS) algorithm which employs the IMU and magnetometers data in an averaging manner. The averaging method is different from a simple smoothing procedure, since it takes the rotations of the platform (during the averaging interval) into account. The proposed AHRS solution is further used to provide additional attitude updates with adaptive noise variances for the integrated INS/GPS system during GPS outages via a refined loosely coupled filtering procedure, making the error growth well restrained. Functionality of the algorithm has been assessed via a field test. The results indicate that the proposed procedure outperforms the traditional integration scheme in different situations, while the latter almost loses track of the movements of the vehicle after 60-second GPS outages. © 2015, Springer-Verlag Berlin Heidelberg.
Allahverdi-zadeh a., ,
Asgari, J.,
Amiri-simkooei, A. Journal of Geodetic Science (20819943)6(1)pp. 93-102
GPS draconitic signal (351.6 ± 0.2 days) and its higher harmonics are observed at almost all IGS products such as position time series of IGS permanent stations. Orbital error and multipath are known as two possible sources of these signals. The effect of Earth shadow crossing of GPS satellites is another suspect for this signal. Up to now there is no serious attempt to investigate this dependence. AMATLAB toolbox is developed and used to determine the satellites located at the earth shadow. RINEX observation files and precise ephemeris are imported to the toolbox and a cylindrical model is used to detect the shadow regions. Data of these satellites were removed from the RINEX observation files of three IGS permanent stations (GRAZ,ONSAandWSRT) and new RINEX observation fileswere created. The time span of these data is about 11 years. The new and original fileswere then processed using precise point positioning (PPP) method to determine position time series, for further analysis. Both the original and new time series were analyzed using the least squares harmonic estimation (LS-HE) in the following steps. The 1st step is the validation of the draconitic harmonics signature in the original position time series of the three stations. The 2nd step does the same for the new time series. It confirms that the power spectrum at the draconitic signals decreases to some extent for the new time series. The difference between the original and new time series (difference between all three position quantity (X, Y and Z)) is then analyzed in the 3rd step. Signature of the draconitic harmonics is also observed to the differences. The results represent that all eight harmonics of GPS draconitic period do exist at the residuals and mainly they decrease. All of the three stations were then processed together using the multivariate LS-HE method. At the 4th step, the difference of the spectral values between the original time series and new times serieswere analyzed. Decreasing of the spectral values at most harmonics (e.g. 1th, 2th, 4th, 6th, 7th and 8th) represents the effect of removing satellite observations at shadow of the earth on draconitic harmonics. At least, five harmonics among seven shows the amelioration of results (draconitic error reduction) after removing the earth shadowed data from RINEX raw data. The results show that the draconitic year's component of data is in part due to eclipsing satellites. © 2016 A. Allahverdi-zadeh et al.
Amiri-simkooei, A.,
Zangeneh-nejad f., F.,
Asgari, J. Journal of Surveying Engineering (07339453)142(3)
Three strategies are employed to estimate the covariance matrix of the unknown parameters in an error-in-variable model. The first strategy simply computes the inverse of the normal matrix of the observation equations, in conjunction with the standard least-squares theory. The second strategy applies the error propagation law to the existing nonlinear weighted total least-squares (WTLS) algorithms for which some required partial derivatives are derived. The third strategy uses the residual matrix of the WTLS estimates applicable only to simulated data. This study investigated whether the covariance matrix of the estimated parameters can precisely be approximated by the direct inversion of the normal matrix of the observation equations. This turned out to be the case when the original observations were precise enough, which holds for many geodetic applications. The three strategies were applied to two commonly used problems, namely a linear regression model and a two-dimensional affine transformation model, using real and simulated data. The results of the three strategies closely followed each other, indicating that the simple covariance matrix based on the inverse of the normal matrix provides promising results that fulfill the requirements for many practical applications. © 2016 American Society of Civil Engineers.
Amiri-simkooei, A.,
Jazaeri, S.,
Zangeneh-nejad f., F.,
Asgari, J. GPS Solutions (10805370)20(1)pp. 51-61
An important step in the high-precision GPS positioning is double-difference integer ambiguity resolution (IAR). The fraction or percentage of success among a number of integer ambiguity fixing is called the success rate. We investigate the ambiguity resolution success rate for the GPS observations for two cases, namely a nominal and a realistic stochastic model of the GPS observables. In principle, one would expect to have higher reliability on IAR success rates if a realistic GPS observables stochastic model is employed. The GPS geometry-based observation model is employed in which a more realistic stochastic model of GPS observables is determined using the least-squares variance component estimation. Two short and one GPS long baseline datasets and one simulated dataset are employed to evaluate the efficacy of the proposed algorithm. The results confirm that a more realistic stochastic model can significantly improve the IAR success rate on individual frequencies, either on L1 or on L2. An improvement of 25 % was achieved to the empirical success rate results. The results are of interest for many applications in which single-frequency observations can be used. This includes applications like attitude determination using single frequency single epoch of GPS observations. © 2015, Springer-Verlag Berlin Heidelberg.
Survey Review (00396265)48(349)pp. 278-286
This contribution presents the weighted total least squares (WTLS) formulation for a mixed errors-in-variables (EIV) model, generally consisting of two erroneous coefficient matrices and two erroneous observation vectors. The formulation is conceptually simple because it is formulated based on the standard least squares theory. It is also flexible because the existing body of knowledge of the least squares theory can directly be generalised to the mixed EIV model. For example, without any derivation, estimate for the variance factor of unit weight and a first approximation for the covariance matrix of the unknown parameters can directly be provided. Further, the constrained WTLS, variance component estimation and the theory of reliability and data snooping can easily be established to the mixed EIV model. The mixed WTLS formulation is also attractive because it can simply handle the two special cases of EIV models: the conditioned EIV model and the parametric EIV model. The WTLS formulation has been applied to three examples. The first two examples are simulated ones, the results of which are shown to be identical to those obtained by the non-linear Gauss-Helmert method. Further, the covariance matrix of the WTLS estimates is shown to closely approximate that obtained through a large number of simulations. The third is a real example of which two object points are photographed by three terrestrial cameras. Three scenarios are employed to show the efficiency of the proposed formulation on this last example. © 2016 Survey Review Ltd.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (16821750)40(1W5)pp. 441-445
NRTK1 is an efficient method to achieve precise real time positioning from GNSS measurements. In this paper we attempt to improve NRTK algorithm by introducing a new strategy. In this strategy a precise relocation of master station observations is performed using Sagnac effect. After processing the double differences, the tropospheric and ionospheric errors of each baseline can be estimated separately. The next step is interpolation of these errors for the atmospheric errors mitigation of desired baseline. Linear and kriging interpolation methods are implemented in this study. In the new strategy the RINEX2 data of the master station is re-located and is converted to the desired virtual observations. Then the interpolated corrections are applied to the virtual observations. The results are compared by the classical method of VRS generation.
Advances in Space Research (02731177)56(6)pp. 1067-1078
GNSS kinematic techniques are capable of providing precise coordinates in extremely short observation time-span. These methods usually determine the coordinates of an unknown station with respect to a reference one. To enhance the precision, accuracy, reliability and integrity of the estimated unknown parameters, GNSS kinematic equations are to be augmented by possible constraints. Such constraints could be derived from the geometric relation of the receiver positions in motion. This contribution presents the formulation of the constrained kinematic global navigation satellite systems positioning. Constraints effectively restrict the definition domain of the unknown parameters from the three-dimensional space to a subspace defined by the equation of motion. To test the concept of the constrained kinematic positioning method, the equation of a circle is employed as a constraint. A device capable of moving on a circle was made and the observations from 11 positions on the circle were analyzed. Relative positioning was conducted by considering the center of the circle as the reference station. The equation of the receiver's motion was rewritten in the ECEF coordinates system. A special attention is drawn onto how a constraint is applied to kinematic positioning. Implementing the constraint in the positioning process provides much more precise results compared to the unconstrained case. This has been verified based on the results obtained from the covariance matrix of the estimated parameters and the empirical results using kinematic positioning samples as well. The theoretical standard deviations of the horizontal components are reduced by a factor ranging from 1.24 to 2.64. The improvement on the empirical standard deviation of the horizontal components ranges from 1.08 to 2.2. © 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.
Zangeneh-nejad f., F.,
Amiri-simkooei, A.,
Sharifi m.a., ,
Asgari, J. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (16821750)40(1W5)pp. 755-761
High-precision GPS positioning requires a realistic stochastic model of observables. A realistic GPS stochastic model of observables should take into account different variances for different observation types, correlations among different observables, the satellite elevation dependence of observables precision, and the temporal correlation of observables. Least-squares variance component estimation (LS-VCE) is applied to GPS observables using the geometry-based observation model (GBOM). To model the satellite elevation dependent of GPS observables precision, an exponential model depending on the elevation angles of the satellites are also employed. Temporal correlation of the GPS observables is modelled by using a first-order autoregressive noise model. An important step in the high-precision GPS positioning is double difference integer ambiguity resolution (IAR). The fraction or percentage of success among a number of integer ambiguity fixing is called the success rate. A realistic estimation of the GNSS observables covariance matrix plays an important role in the IAR. We consider the ambiguity resolution success rate for two cases, namely a nominal and a realistic stochastic model of the GPS observables using two GPS data sets collected by the Trimble R8 receiver. The results confirm that applying a more realistic stochastic model can significantly improve the IAR success rate on individual frequencies, either on L1 or on L2. An improvement of 20% was achieved to the empirical success rate results. The results also indicate that introducing the realistic stochastic model leads to a larger standard deviation for the baseline components by a factor of about 2.6 on the data sets considered.
Amiri-simkooei, A.,
Zangeneh-nejad f., F.,
Asgari, J.,
Jazaeri, S. Measurement: Journal of the International Measurement Confederation (02632241)48(1)pp. 378-386
Linear regression problem is a widely used problem in many metrological and measurement systems. This contribution presents a simple and reliable formulation for the linear regression fit using the weighted total least squares (WTLS) problem, when both variables are subjected to different and possibly correlated noise. The formulation is a follow up to four recent research papers in which the method was successfully applied to errors-in-variables models. It is a simple modification of the standard least squares method whose principal result is that the so-called perpendicular offsets are minimized when the full structure of correlated noise among all elements of variable x, y or both variables is supposed to be used. The formulation is rigorous, thus without approximation, and can directly provide the uncertainty of the estimated parameters. In a special case, the general formulation simplifies to the well-known standard linear regression model available in the literature. The effectiveness of the algorithm, which was implemented in MATLAB and is available in Appendix A, is demonstrated using three simulated and experimental data sets. The results indicate that accurate and reliable estimates of line parameters along with their covariance matrix can be provided using the proposed formulation in a relatively small amount of time. © 2013 Elsevier B.V. All rights reserved.
Amiri-simkooei, A.,
Zangeneh-nejad f., F.,
Asgari, J. Journal of Surveying Engineering (07339453)139(4)pp. 176-187
To achieve the best linear unbiased estimation of unknown parameters in geodetic data processing a realistic stochastic model for observables is required. This work is a follow-up to work carried out recently in which the geometry-free observation model (GFOM) was used. Here, least-squares variance component estimation is applied to global positioning system (GPS) observables using the geometry-based observation model (GBOM). The benefit of using GBOM, rather than GFOM, is highlighted in the present contribution. An appropriate stochastic model for GPS observables should include different variances for each observation type, the correlation between different observables, the satellite elevation dependence of the observables' precision, and the temporal correlation of the GPS observables. Unlike the GFOM, in theGBOMtwo separate variances along with their corresponding covariances are simultaneously estimated for the phase observations of the L1 and L2 frequencies. The numerical results for two receivers-namely, Trimble 4000 SSi (Trimble Navigation, Sunnyvale, California) and Leica SR530 (Leica Geosystems, Aarau, Switzerland)-indicate a significant correlation between the observation types. The results show positive correlations of 0.55 and 0.51 between the CA and P2 code observations for Trimble 4000 SSi and Leica SR530, respectively. In addition, the satellites' elevation dependence of the GPS observables' precision is remarkable. Also, a temporal correlation of about 10 s exists in the L2 GPS observables for the Trimble 4000 SSi receiver. © 2013 American Society of Civil Engineers.
Amiri-simkooei, A.,
Asgari, J.,
Zangeneh-nejad f., F.,
Zaminpardaz s., Journal of Surveying Engineering (07339453)138(4)pp. 172-183
This contribution reviews a few basic concepts of optimization and design of a geodetic network. Proper assessment and analysis of networks is an important task in many geodetic-surveying projects. Appropriate quality-control measures should be defined, and an optimal design should be sought. The quality of a geodetic network is characterized by precision, reliability, and cost. The aim is to present a few case studies that have been designed to meet optimal precision and reliability criteria. Though the case studies may be of interest to the geodetic community in their own right, the aim is to gain insight into the general optimization problem of a geodetic network. This is also potentially of interest for educational purposes. The case studies include a zeroth-order design to improve the precision of the network points in a traverse network and a first-order design to meet the high reliability and maximum precision criteria in a geodetic network. It is shown that not only the configuration of the network but also the type of the observations used can affect the design criteria. For example, the case studies presented show that the optimal shape of the trilateration network (intersection with distances) can result in a weak network in the sense of reliability and precision if the observations are replaced by angles rather than distances (triangulation network). In close relation to the optimization problem of a geodetic network, the global positioning system satellite configuration is also optimized for a particular case that provides the minimum value of the geometric dilution of precision. © 2012 American Society of Civil Engineers.
GPS Solutions (15211886)16(1)pp. 77-88
In an attempt to model regular variations of the ionosphere, the least-squares harmonic estimation is applied to the time series of the total electron contents (TEC) provided by the JPL analysis center. Multivariate and modulated harmonic estimation spectra are introduced and estimated for the series to detect the regular and modulated dominant frequencies of the periodic patterns. Two significant periodic patterns are the diurnal and annual signals with periods of 24/n hours and 365. 25/n days (n = 1, 2,...), which are the Fourier series decomposition of the regular daily and yearly periodic variations of the ionosphere. The spectrum shows a cluster of periods near 27 days, thereby indicating irregularities at this solar cycle period. A series of peaks, with periods close to the diurnal signal and its harmonics, are evident in the spectrum. In fact, the daily signal harmonics of ω i = 2πi are modulated with the annual signal harmonics of ω j = 2πj/365. 25 as ω ijM = 2πi(1 ± j/365. 25i). Among them, at low and midlatitudes, the largest variations belong to the diurnal signal modulated to the semiannual signal. Some preliminary results on the modulated part are presented. The maximum ranges of the modulated daily signal are ±15 TECU and ±6 TECU at high and low solar periods, respectively. A model consisting of purely harmonic functions plus modulated ones is capable of studying known regular anomalies of the ionosphere, which is currently in progress. © 2011 The Author(s).
Journal of the Earth and Space Physics (2538371X)37(1)pp. 11-24
The least squares harmonic estimation is applied to the hourly time-series of Total Electron Contents (TEC) derived from ionospheric models using seven years of GPS observations processed by Bernese software. The frequencies of dominant spectral components in the spectrum are estimated. We observe significant periodic patterns with periods of 24 h and its fractions 24h/n, n=2,.,11, which are the well-known Fourier series decomposition of the diurnal periodic pattern of the ionospheric variations. The principal component with daily signal is due to the day-night variation of TEC values. The semidiurnal and tri-diurnal components can be explained by the substorm signatures in both auroral electrojet (in layer E) and ring current variations (related to magnetosphere at low latitudes) and tidal effects. Also, the spectrum shows the well-known 27-day period of solar cycle variations. We observe annual, semi-annual and tri-annual signals in the series. The detected signals are then applied to perform an ionospheric prediction. The results indicate that a substantial part (in the absolute sense) of the TEC values can be predicted using this base function, and an undetectable part remains as disturbed noise which can exceed 20 TEC units for the disturbed ionosphere. In comparison with the standard Klobuchar model, the model presented in this contribution will significantly improve the single frequency GPS positioning accuracy.
