Journal of Applied Geodesy最新文献_第4页

Regional GPS orbit determination using code-based pseudorange measurement with residual correction model 基于代码的残差校正伪距测量区域GPS定轨

Journal of Applied Geodesy Pub Date : 2023-10-27 DOI: 10.1515/jag-2023-0044

Hong Sheng Lee, Wan Anom Wan Aris, Tajul Ariffin Musa, Ahmad Zuri Sha’ameri, Ooi Wei Han, Dong-Ha Lee, Mohammad Asrul Mustafar

{"title":"Regional GPS orbit determination using code-based pseudorange measurement with residual correction model","authors":"Hong Sheng Lee, Wan Anom Wan Aris, Tajul Ariffin Musa, Ahmad Zuri Sha’ameri, Ooi Wei Han, Dong-Ha Lee, Mohammad Asrul Mustafar","doi":"10.1515/jag-2023-0044","DOIUrl":"https://doi.org/10.1515/jag-2023-0044","url":null,"abstract":"Abstract The study introduces the concept of regional GPS orbit determination, whereby GPS satellite positions are determined using GPS measurements from locally or regional distributed stations. The importance and characteristics of regional GPS orbit are briefly discussed. The technique used to determine the regional GPS satellite position is coined Inverse Single Point Positioning (ISPP). Code-based pseudorange is used and improved using residual correction model. Two designs of station distribution are selected in this study, which only cover stations in Malaysia and stations situated 8000 km from a reference point in Malaysia. The root-mean-squared-error (RMSE) of ISPP when compared against final ephemeris were 660.65 m and 27.61 m, while the 3D RMSE of positioning were 1.612 m and 1.324 m for the first and second designs, respectively, lower than the accuracy of broadcast ephemeris. Three parameters are identified as factors affecting accuracy of ISPP, namely geometry of station distribution, nature of measurement used, and technique of orbit determination. Further research will be required to fully realize a functional regional GPS orbit.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262112","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}

引用次数: 0

Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine) 第聂伯罗和德涅斯特瀑布水电站水工结构空间位移和变形监测(乌克兰)

Journal of Applied Geodesy Pub Date : 2023-10-10 DOI: 10.1515/jag-2023-0021

Korneliy Tretyak, Yuriy Bisovetskyi, Ihor Savchyn, Tetiana Korlyatovych, Oleg Chernobyl, Sergey Kukhtarov

引用次数: 0

A machine-learning approach to estimate satellite-based position errors 一种估计卫星定位误差的机器学习方法

Journal of Applied Geodesy Pub Date : 2023-10-06 DOI: 10.1515/jag-2023-0051

Anil Kumar Ramavath, Naveen Kumar Perumalla

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Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017 利用FFNN预测2021年和2022年5次不同X级太阳耀斑的电离层TEC，并与COKSM和IRI PLAS 2017进行比较

Journal of Applied Geodesy Pub Date : 2023-10-05 DOI: 10.1515/jag-2023-0057

Sarat C. Dass, Raju Mukesh, Muthuvelan Vijay, Sivavadivel Kiruthiga, Shunmugam Mythili

{"title":"Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017","authors":"Sarat C. Dass, Raju Mukesh, Muthuvelan Vijay, Sivavadivel Kiruthiga, Shunmugam Mythili","doi":"10.1515/jag-2023-0057","DOIUrl":"https://doi.org/10.1515/jag-2023-0057","url":null,"abstract":"Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134948261","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}

引用次数: 0

PPP_Mansoura: an open-source software for multi-constellation GNSS processing PPP_Mansoura:多星座GNSS处理开源软件

Journal of Applied Geodesy Pub Date : 2023-10-05 DOI: 10.1515/jag-2023-0043

Islam A. Kandil, Ahmed A. Awad, Mahmoud El-Mewafi

引用次数: 0

Analysis of differential code biases for GPS receivers over the Indian region 印度地区GPS接收机差分码偏差分析

Journal of Applied Geodesy Pub Date : 2023-10-05 DOI: 10.1515/jag-2023-0047

Kondaveeti Sivakrishna, Devanaboyina Venkata Ratnam

{"title":"Analysis of differential code biases for GPS receivers over the Indian region","authors":"Kondaveeti Sivakrishna, Devanaboyina Venkata Ratnam","doi":"10.1515/jag-2023-0047","DOIUrl":"https://doi.org/10.1515/jag-2023-0047","url":null,"abstract":"Abstract The GPS Aided Geo Augmented Navigation (GAGAN) system provides the navigational services for single-frequency GNSS user via broadcasting the differential corrections with GEO stationary satellites. The significant differential correction contribution comes from ionospheric time delays and is necessary to be determined precisely. Dual-frequency GPS receivers measure the ionospheric time delays using GPS code and carrier phase measurements. The determination of absolute ionospheric Total Electron Content (TEC) requires the calibration of GPS satellites and receiver hardware biases due to different frequency-dependent signals (L1 and L2) due to environmental changes (Temperature and Humidity). In this paper, A receiver-based Differential Code Biases (DCB) algorithm is implemented to derive a joint estimation of TEC and RDCB parameters using the weighted Least Square (WLS) method. The daily averaged DCBs data for 26 GPS receivers are obtained for 3 years (2014–2016) from 26 GPS reeivers over Indian region. The receiver DCB algorithmis validated with the Fitted Receiver Biases (FRB) method. The correlation (R) between VTEC and RDCB is conducted to investigate the RDCB stability. The results would be useful for the accurate determination of ionospheric differential corrections to GAGAN users.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947922","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}

引用次数: 0

Keypoint-based registration of TLS point clouds using a statistical matching approach 使用统计匹配方法的基于关键点的TLS点云配准

Journal of Applied Geodesy Pub Date : 2023-09-25 DOI: 10.1515/jag-2022-0058

Jannik Janßen, Heiner Kuhlmann, Christoph Holst

{"title":"Keypoint-based registration of TLS point clouds using a statistical matching approach","authors":"Jannik Janßen, Heiner Kuhlmann, Christoph Holst","doi":"10.1515/jag-2022-0058","DOIUrl":"https://doi.org/10.1515/jag-2022-0058","url":null,"abstract":"Abstract Laser scanning is a wide-spread practice to capture the environment. Besides the fields of robotics and self-driving cars, it has been applied in the field of engineering geodesy for documentation and monitoring purposes for many years. The registration of scans is still one of the main sources of uncertainty in the final point cloud. This paper presents a new keypoint-based method for terrestrial laser scan (TLS) registration for high-accuracy applications. Based on detected 2D-keypoints, we introduce a new statistical matching approach that tests wheter keypoints, scanned from two scan stations, can be assumed to be identical. This approach avoids the use of keypoint descriptors for matching and also handles wide distances between different scanner stations. The presented approach requires a good coarse registration as initial input, which can be achieved for example by artificial laser scanning targets. By means of two evaluation data sets, we show that our keypoint-based registration leads to the smallest loop closure error when traversing several stations compared to target-based and ICP registrations. Due to the high number of observations compared to the target-based registration, the reliability of the our keypoint-based registration can be increased significantly and the precision of the registration can be increased by about 25 % on average.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135768501","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}

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Frontmatter 头版头条

Journal of Applied Geodesy Pub Date : 2023-09-22 DOI: 10.1515/jag-2023-frontmatter4

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A proposed neural network model for obtaining precipitable water vapor 提出了一种获取可降水量的神经网络模型

Journal of Applied Geodesy Pub Date : 2023-09-14 DOI: 10.1515/jag-2023-0035

Hadeer Al-Eshmawy, Mohamed A. Abdelfatah, Gamal S. El-Fiky

{"title":"A proposed neural network model for obtaining precipitable water vapor","authors":"Hadeer Al-Eshmawy, Mohamed A. Abdelfatah, Gamal S. El-Fiky","doi":"10.1515/jag-2023-0035","DOIUrl":"https://doi.org/10.1515/jag-2023-0035","url":null,"abstract":"Abstract The atmospheric Precipitable water vapor (PWV) is a variable key for weather forecasting and climate change. It is a considerable component of the atmosphere, influencing numerous atmospheric processes, and having physical characteristics. It can be measured directly using radiosonde stations (RS), which are not always accessible and difficult to measure with acceptable spatial and time precision. This study uses the artificial neural network (ANN) application to propose a simple model based on RS data to estimate PWV from surface metrological data. Ten RS stations were used to develop the new model for eight and a half years. In addition, two and a half years of data were used to validate the developed model. The study period is based on the data accessible between 2010 and 2020. The new model needs to collect (vapor pressure, temperature, latitude, longitude, height, day of year, and relative humidity) as input parameters in ANN to predict the PWV. The ANN model validations were based on the root mean square (RMS), correlation coefficient (CC), and T-test. According to the results, the proposed ANN can accurately predict the PWV over Egypt. The results of the new ANN model and eight other empirical models (Saastamoinen, Askne and Nordius, Okulov et al., Maghrabi et al., Phokate., Falaiye et al. (A&B), Qian et al. and ERA 5) are compared in addition, the new PWV model can achieve the best performance with RMS of 0.21 mm. The new model can serve as a will be of practical utility with a high degree of precision in PWV estimation.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911308","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}

引用次数: 0

Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations 基于集成的GPS和气象观测综合水汽(IWV)预测模型

Journal of Applied Geodesy Pub Date : 2023-09-12 DOI: 10.1515/jag-2023-0053

Nirmala Bai Jadala, Miriyala Sridhar, Devanaboyina Venkata Ratnam, Surya Narayana Murthy Tummala

{"title":"Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations","authors":"Nirmala Bai Jadala, Miriyala Sridhar, Devanaboyina Venkata Ratnam, Surya Narayana Murthy Tummala","doi":"10.1515/jag-2023-0053","DOIUrl":"https://doi.org/10.1515/jag-2023-0053","url":null,"abstract":"Abstract Integrated water vapor (IWV) has been widely perceived through machine learning (ML) strategies. During this investigation, we employed IWV time series from weather stations to determine the oscillations and patterns with IWV across two latitudes namely VBIT, Hyderabad (India) and PWVUO station, Oregon (US). The GPS derived IWV and meteorological data such as pressure ( P ), temperature ( T ) and relative humidity (RH) dataset for the year 2014 has been taken from VBIT station and from PWVUO station for 2020. Five machine learning algorithms namely Optimized Ensemble (OE) model, Rational Quadratic Gaussian Process Regression model (RQ-GPR), Neural Networks model (NN), Cubic Support Vector Machine (CSVM) and Quadratic Support Vector Machine (QSVM) algorithms are used. The GPS derived IWV data revealed the maximum variation during summer monsoon period specifically in the month of July. The correlation analysis between GPS-IWV and optimized ensemble technique showed the highest correlation for the VBIT station with correlation coefficient as ( ρ ) = 99 % and at PWVUO station as ( ρ ) = 88 % for two different datasets. The residual analysis has also showed less variation to the optimized ensemble model. The performance metrics obtained for OE at VBIT station are mean absolute error (MAE) as 0.64 kg/m 2 , mean absolute percentage error (MAPE) as 3.80 % and root mean squared error (RMSE) as 0.94 kg/m 2 and at PWVUO station the values are MAE = 1.91 kg/m 2 , MAPE = 11.76 % and RMSE as 1.97 kg/m 2 , respectively. The results explained that the OE method has shown a better performance compared to the remaining models.","PeriodicalId":45494,"journal":{"name":"Journal of Applied Geodesy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135825620","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}

引用次数: 0