K. Maciuk, S. Nistor, Ivan Brusak, P. Lewińska, J. Kudrys
Abstract With the advent of the Global Navigation Satellite System (GNSS), the need for precise and highly accurate orbit and clock products becomes crucial in processing GNSS data. Clocks in GNSS observations form the basis of positioning. Their high quality and stability enable high accuracy and the reliability of the obtained results. The clock modelling algorithms are continuously improved; thus, the accuracy of the clock products is evolving. At present, 8 Analysis Centers (ACs) contribute to the International GNSS Service final clock products. These products are based on GNSS observations on a network of reference stations, where for a given day one of the reference station clocks is the reference clock. In this paper, the authors determined the impact of the reference clock on the quality of clock product, especially outliers, for the first time. For this purpose, the multi-GNSS final clock products provided by the Center for Orbit Determination in Europe (CODE) for the period 2014–2021 (1773–2190 GPS week, 2921 days) were analysed. Analysis shows that by applying the Median Absolute Deviation (MAD) algorithm for outlier detection, the Passive Hydrogen Maser (PHM) clock installed on board the GALILEO satellites have the lowest level of noise, whereas the Block IIR GPS satellite launched in 1999 appears to have the highest levels of noise. Furthermore, the GNSS station OHIE3, when used as a reference clock, generates an increase in the level of noise, especially noticeable on the G09 and E03 satellites.
{"title":"Reference clock impact on GNSS clock outliers","authors":"K. Maciuk, S. Nistor, Ivan Brusak, P. Lewińska, J. Kudrys","doi":"10.1515/jag-2023-0007","DOIUrl":"https://doi.org/10.1515/jag-2023-0007","url":null,"abstract":"Abstract With the advent of the Global Navigation Satellite System (GNSS), the need for precise and highly accurate orbit and clock products becomes crucial in processing GNSS data. Clocks in GNSS observations form the basis of positioning. Their high quality and stability enable high accuracy and the reliability of the obtained results. The clock modelling algorithms are continuously improved; thus, the accuracy of the clock products is evolving. At present, 8 Analysis Centers (ACs) contribute to the International GNSS Service final clock products. These products are based on GNSS observations on a network of reference stations, where for a given day one of the reference station clocks is the reference clock. In this paper, the authors determined the impact of the reference clock on the quality of clock product, especially outliers, for the first time. For this purpose, the multi-GNSS final clock products provided by the Center for Orbit Determination in Europe (CODE) for the period 2014–2021 (1773–2190 GPS week, 2921 days) were analysed. Analysis shows that by applying the Median Absolute Deviation (MAD) algorithm for outlier detection, the Passive Hydrogen Maser (PHM) clock installed on board the GALILEO satellites have the lowest level of noise, whereas the Block IIR GPS satellite launched in 1999 appears to have the highest levels of noise. Furthermore, the GNSS station OHIE3, when used as a reference clock, generates an increase in the level of noise, especially noticeable on the G09 and E03 satellites.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44237946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper deals with applications of digital imaging total stations in a geodetic context using artificial intelligence (AI). We present two different use cases. The first is to minimise manual intervention by the operator by classifying images with different backgrounds. We use a developed software to control a total station extended by an industrial camera, which is used for the in-situ calibration of the camera. We show that the AI successfully tests the captured image for its suitability for further use and under which circumstances the AI fails. The second case is the detection of different geodetic targets (reflective and non-reflective). Captured images of an imaging total station are automatically checked to see whether a supposed target is shown in the image, identify it and localise it in the image. Already implemented applications for target identification are to be supported in this way and extended by further information.
{"title":"Classification and object detection with image assisted total station and machine learning","authors":"Kira Zschiesche, Martin Schlüter","doi":"10.1515/jag-2023-0011","DOIUrl":"https://doi.org/10.1515/jag-2023-0011","url":null,"abstract":"Abstract This paper deals with applications of digital imaging total stations in a geodetic context using artificial intelligence (AI). We present two different use cases. The first is to minimise manual intervention by the operator by classifying images with different backgrounds. We use a developed software to control a total station extended by an industrial camera, which is used for the in-situ calibration of the camera. We show that the AI successfully tests the captured image for its suitability for further use and under which circumstances the AI fails. The second case is the detection of different geodetic targets (reflective and non-reflective). Captured images of an imaging total station are automatically checked to see whether a supposed target is shown in the image, identify it and localise it in the image. Already implemented applications for target identification are to be supported in this way and extended by further information.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45895003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract One of the primary geodetic mapping tasks in the post-processing of GNSS data is precise point positioning (PPP). Research institutions and universities have established software packages and online PPP services in prior years. Still, it is problematic to satisfy the high-rate update criterion of PPP due to the quick growth of GNSS constellations. In PPP GNSS data processing, Square Root Information Filter SRIF is not frequently handled. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. The new program has been demonstrated how can effectively enhance initial time and positioning error for multi-GNSS satellites. Processing observation data with the Kalman filter and SRIF was performed using PPP in static mode for the 16 stations, and the influence of errors has been analyzed from the filtering method. The Kalman filter was unable to maintain a stable convergence curve during the PPP filtering procedure, but SRIF was successful in doing so. Based on these findings, SRIF had better numerical stability and was well-suited for settings with PPP demanding precision computing environments.
{"title":"Implementing SRIF filter with MANS-PPP software package for GNSS precise point position solution accuracy enhancement","authors":"Ashraf G. Shehata, F. Zarzoura, Mahmoud El-Mewafi","doi":"10.1515/jag-2023-0017","DOIUrl":"https://doi.org/10.1515/jag-2023-0017","url":null,"abstract":"Abstract One of the primary geodetic mapping tasks in the post-processing of GNSS data is precise point positioning (PPP). Research institutions and universities have established software packages and online PPP services in prior years. Still, it is problematic to satisfy the high-rate update criterion of PPP due to the quick growth of GNSS constellations. In PPP GNSS data processing, Square Root Information Filter SRIF is not frequently handled. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. The new program has been demonstrated how can effectively enhance initial time and positioning error for multi-GNSS satellites. Processing observation data with the Kalman filter and SRIF was performed using PPP in static mode for the 16 stations, and the influence of errors has been analyzed from the filtering method. The Kalman filter was unable to maintain a stable convergence curve during the PPP filtering procedure, but SRIF was successful in doing so. Based on these findings, SRIF had better numerical stability and was well-suited for settings with PPP demanding precision computing environments.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48933889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Basma E. Mohamed, Heba S. Tawfik, M. Abdelfatah, G. El-fiky
Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T-test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.
{"title":"Improvement of international reference ionospheric model total electron content maps: a case study using artificial neural network in Egypt","authors":"Basma E. Mohamed, Heba S. Tawfik, M. Abdelfatah, G. El-fiky","doi":"10.1515/jag-2023-0002","DOIUrl":"https://doi.org/10.1515/jag-2023-0002","url":null,"abstract":"Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T-test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48342041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term “quality” is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).
{"title":"Automatic quality assessment of terrestrial laser scans","authors":"J. Hartmann, Max Heiken, H. Alkhatib, I. Neumann","doi":"10.1515/jag-2022-0030","DOIUrl":"https://doi.org/10.1515/jag-2022-0030","url":null,"abstract":"Abstract This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term “quality” is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49182305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Positioning performance validates a smartphone’s antenna and chipset performance. This paper attempts to characterize the positioning performance of smartphones with dual-frequency GNSS chipsets compared to their counterparts using single-frequency chipsets. Furthermore, investigate the positioning performance of dual-frequency measurements using the NavIC L5 signal. A comparison of satellite geometry and position solution accuracy in relation to a known reference station is performed. The single-frequency positioning accuracy with 2DRMS (horizontal root mean square error) is about 5.11 m. Although, 2DRMS with dual-frequency measurements is 2.0 m. However, with the integration of NavIC with dual frequency measurements, the best position accuracy with 2DRMS is about 1.93 m over the observed location. It has been observed that single-frequency measurements can offer a MRSE (mean radial spherical error) precision of 2.75 m, whereas their dual-frequency counterparts can yield a precision of about 1.11 m, and with NavIC integration, it is around 1.05 m. During the observations, it should be noted that the NavIC L5 signals have modestly enhanced the positioning precision of smartphones. This analysis could be useful for fundamental Android GNSS research, smartphone positioning service and training communities.
{"title":"Assessment of android smartphones positioning in multi-GNSS/NavIC environment","authors":"D. Kuna, Naveen Kumar Perumalla","doi":"10.1515/jag-2022-0062","DOIUrl":"https://doi.org/10.1515/jag-2022-0062","url":null,"abstract":"Abstract Positioning performance validates a smartphone’s antenna and chipset performance. This paper attempts to characterize the positioning performance of smartphones with dual-frequency GNSS chipsets compared to their counterparts using single-frequency chipsets. Furthermore, investigate the positioning performance of dual-frequency measurements using the NavIC L5 signal. A comparison of satellite geometry and position solution accuracy in relation to a known reference station is performed. The single-frequency positioning accuracy with 2DRMS (horizontal root mean square error) is about 5.11 m. Although, 2DRMS with dual-frequency measurements is 2.0 m. However, with the integration of NavIC with dual frequency measurements, the best position accuracy with 2DRMS is about 1.93 m over the observed location. It has been observed that single-frequency measurements can offer a MRSE (mean radial spherical error) precision of 2.75 m, whereas their dual-frequency counterparts can yield a precision of about 1.11 m, and with NavIC integration, it is around 1.05 m. During the observations, it should be noted that the NavIC L5 signals have modestly enhanced the positioning precision of smartphones. This analysis could be useful for fundamental Android GNSS research, smartphone positioning service and training communities.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43622345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Integrated GPS/INS using Kalman filter is the best technique for improving navigation accuracy. Assuming that the covariance matrices are known and constant, a conventional Kalman filter (CKF) is usually used, however, when they are unknown and time-varying, several adaptive estimation approaches have to be developed to estimate the statistical information of the measurement (R), process (Q), and state (P) covariance matrices. In many situations, blunders/faults in the measurement model and/or sudden changes in the dynamic model may occur during the navigation period. Therefore, the CKF, as well as the adaptive Kalman filter (AKF) will exhibit abnormal behavior and may lead the filter to be suboptimal or even diverge. In this study, the Sage-Husa adaptive Kalman filter (SHAKF) and innovation-based adaptive Kalman filter (IAKF) approaches are employed for adapting the measurement covariance matrix(R). In the case of abrupt changes in the dynamic model, the state covariance matrix (P) is adapted using the strong tracking filter (STF). The performance of these adaptive approaches is evaluated before and after simulating a fault of different sizes in the measurement and dynamic models. The results show that with a large window width, the SHAKF outperforms the CKF and IAKF. However, when the system encounters any fault either in the measurement or dynamic model, the SHAKF loses its optimality and diverges. The sensitivity of the SHAKF to the fault is because the R matrix accumulates with the propagation of the recursive noise estimator. On the other hand, the IAKF and STF provide better performance than both the CKF and SHAKF because the gain matrix is adaptively adjusted to mitigate the influence of the fault, and therefore, they behave normally when a fault of any size occurs in the measurement and/or dynamic model.
{"title":"An integrated adaptive Kalman filter for improving the reliability of navigation systems","authors":"A. Almagbile, Jinling Wang, A. Al-Rawabdeh","doi":"10.1515/jag-2022-0048","DOIUrl":"https://doi.org/10.1515/jag-2022-0048","url":null,"abstract":"Abstract Integrated GPS/INS using Kalman filter is the best technique for improving navigation accuracy. Assuming that the covariance matrices are known and constant, a conventional Kalman filter (CKF) is usually used, however, when they are unknown and time-varying, several adaptive estimation approaches have to be developed to estimate the statistical information of the measurement (R), process (Q), and state (P) covariance matrices. In many situations, blunders/faults in the measurement model and/or sudden changes in the dynamic model may occur during the navigation period. Therefore, the CKF, as well as the adaptive Kalman filter (AKF) will exhibit abnormal behavior and may lead the filter to be suboptimal or even diverge. In this study, the Sage-Husa adaptive Kalman filter (SHAKF) and innovation-based adaptive Kalman filter (IAKF) approaches are employed for adapting the measurement covariance matrix(R). In the case of abrupt changes in the dynamic model, the state covariance matrix (P) is adapted using the strong tracking filter (STF). The performance of these adaptive approaches is evaluated before and after simulating a fault of different sizes in the measurement and dynamic models. The results show that with a large window width, the SHAKF outperforms the CKF and IAKF. However, when the system encounters any fault either in the measurement or dynamic model, the SHAKF loses its optimality and diverges. The sensitivity of the SHAKF to the fault is because the R matrix accumulates with the propagation of the recursive noise estimator. On the other hand, the IAKF and STF provide better performance than both the CKF and SHAKF because the gain matrix is adaptively adjusted to mitigate the influence of the fault, and therefore, they behave normally when a fault of any size occurs in the measurement and/or dynamic model.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46215990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fitore Bajrami Lubishtani, B. Idrizi, Milot Lubishtani
Abstract For the development of various geodetic tasks within a state, determining the Height Reference Surface by the geoid model is extremely important. Considering this, one of the main task of geodesy is to determine the geoid, which is defined as an equipotential surface of the Earth’s gravity field, as a result, it corresponds on average to the sea level. The aim of this study is to analyze the best-fitting geoid model for the territory of the Republic of Albania. In this study, DFHRS (Digital Finite Element Height Reference Surface) method was used (www.dfhbf.de), developed by Reiner Jäger [Jäger R. State of the art and present developments of a general approach for GPS-based height determination. East Africa: University of Applied Sciences, Faculty Geoinformationswesen, Department of Surveying and Geomatics. Paper Presented at the First Workshop on GPS and Mathematical Geodesy in Tanzania (Kilimanjaro Expedition 1999); 1999] to determine the most suitable geoid model for the territory of Albania. This approach allows the conversion of ellipsoidal heights determined by GNSS into the standard heights, which refer to the height reference surface (HRS) of an orthometric. The DFHRS is defined as continuous HRS in arbitrarily large areas by bivariate polynomials over an irregular grid [Jäger R, Schneid S. Online and postprocessed GPS heighting based on the concept of a digital height reference surface (DFHRS), in vertical reference systems. In: IAG Symposium. Cartagena, Colombia, Heidelberg: Springer; 2001, vol 124:203–8 pp]. The DFHRS approach uses a wide range of input data (Geometric and Physical) and in our case, there were 151 GPS/levelling height data as well as physical derivatives from different global geopotential models. The main focus of this study is placed on the calculation of the most suitable geoid model for the territory of Albania using global geopotential models (EGM96, EGM2008, EIGEN04, EIGEN6C4 and European Gravimetric Geoid Model 1997 (EGG97)). After analyzing the results and comparing the models among themselves, the Albanian DFHRS-EIGEN6C4 model was selected as the most suitable model for the territory of Albania.
{"title":"Determination of the height reference surface for the Republic of Albania by using global geopotential models","authors":"Fitore Bajrami Lubishtani, B. Idrizi, Milot Lubishtani","doi":"10.1515/jag-2022-0061","DOIUrl":"https://doi.org/10.1515/jag-2022-0061","url":null,"abstract":"Abstract For the development of various geodetic tasks within a state, determining the Height Reference Surface by the geoid model is extremely important. Considering this, one of the main task of geodesy is to determine the geoid, which is defined as an equipotential surface of the Earth’s gravity field, as a result, it corresponds on average to the sea level. The aim of this study is to analyze the best-fitting geoid model for the territory of the Republic of Albania. In this study, DFHRS (Digital Finite Element Height Reference Surface) method was used (www.dfhbf.de), developed by Reiner Jäger [Jäger R. State of the art and present developments of a general approach for GPS-based height determination. East Africa: University of Applied Sciences, Faculty Geoinformationswesen, Department of Surveying and Geomatics. Paper Presented at the First Workshop on GPS and Mathematical Geodesy in Tanzania (Kilimanjaro Expedition 1999); 1999] to determine the most suitable geoid model for the territory of Albania. This approach allows the conversion of ellipsoidal heights determined by GNSS into the standard heights, which refer to the height reference surface (HRS) of an orthometric. The DFHRS is defined as continuous HRS in arbitrarily large areas by bivariate polynomials over an irregular grid [Jäger R, Schneid S. Online and postprocessed GPS heighting based on the concept of a digital height reference surface (DFHRS), in vertical reference systems. In: IAG Symposium. Cartagena, Colombia, Heidelberg: Springer; 2001, vol 124:203–8 pp]. The DFHRS approach uses a wide range of input data (Geometric and Physical) and in our case, there were 151 GPS/levelling height data as well as physical derivatives from different global geopotential models. The main focus of this study is placed on the calculation of the most suitable geoid model for the territory of Albania using global geopotential models (EGM96, EGM2008, EIGEN04, EIGEN6C4 and European Gravimetric Geoid Model 1997 (EGG97)). After analyzing the results and comparing the models among themselves, the Albanian DFHRS-EIGEN6C4 model was selected as the most suitable model for the territory of Albania.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41954941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Omidalizarandi, Bahareh Mohammadivojdan, H. Alkhatib, J. Paffenholz, I. Neumann
Abstract Today, rapid growth in infrastructure development and urbanisation process increases the attention for accurate deformation monitoring on a relatively large-scale. Furthermore, such deformation monitoring is of great importance in the assessment and management of natural hazard processes like landslides, earthquakes, and floods. In this study, the Persistent Scatterer Interferometry (PSI) technique is applied using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. It allows point-wise deformation monitoring based on time series analysis of specific points. It also enables performing spatio-temporal area-based deformation monitoring. Currently, these data do not have a sophisticated quality assurance process to judge the significance of deformations. To obtain different quality classes of the Persistent Scatterer (PS) data points, the first step is to classify them into buildings and ground types using LoD2 building models. Next, time series analysis of the PS points is performed to model systematic and random errors. It allows estimation of the offset and the deformation rate for each point. Finally, spatio-temporal modelling of neighbourhood relations of the PS points is carried out using local geometric patches which are approximated with a mathematical model, such as, e.g., multilevel B-Splines. Subsequently, the quality of SAR data from temporal and spatial neighbourhood relations is checked. Having an appropriate spatio-temporal quality model of the PS data, a deformation analysis is performed for areas of interest in the city of Hamburg. In the end, the results of the deformation analysis are compared with the BodenBewegungsdienst Deutschland (Ground Motion Service Germany) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.
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