Journal articles: 'GPS data processing'

1

Smith, M. S., and G. S. Ladde. "Processing of filtered GPS data." IEEE Transactions on Aerospace and Electronic Systems 25, no. 5 (1989): 711–28. http://dx.doi.org/10.1109/7.42095.

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Yongjun, Zhang. "Combined GPS/GLONASS data processing." Geo-spatial Information Science 5, no. 4 (January 2002): 32–36. http://dx.doi.org/10.1007/bf02826472.

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Yunfeng, Tian. "Data-processing induced GPS-positioning error." Geodesy and Geodynamics 3, no. 3 (August 2012): 51–56. http://dx.doi.org/10.3724/sp.j.1246.2012.00051.

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Liu, Zhao Jun. "New Data Processing Method Research on GLONASS." Applied Mechanics and Materials 599-601 (August 2014): 1580–83. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1580.

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Because GLONASS and GPS are different in system, data processing of GLONASS carrier phase difference is quite different from that of GPS, requiring special methods. Based on eliminating the impact of the relative deviation of receiver clock, a new mathematical model of GLONASS phase difference is introduced in this article. This new model can make full use of existing GPS data processing technology to complete GLONASS data processing work conveniently.

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Shen, Li, and Peter R. Stopher. "Review of GPS Travel Survey and GPS Data-Processing Methods." Transport Reviews 34, no. 3 (April 4, 2014): 316–34. http://dx.doi.org/10.1080/01441647.2014.903530.

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Syetiawan, Agung. "BLUNDER PENGOLAHAN DATA GPS." JURNAL ILMIAH GEOMATIKA 22, no. 2 (May 24, 2017): 72. http://dx.doi.org/10.24895/jig.2016.22-2.641.

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ABSTRAKPengamatan satelit menghasilkan koordinat posisi berdasarkan pada perjalanan sinyal dari satelit ke antenna yang ada di Bumi. Pada perjalanannya, sinyal satelit tersebut mengalami berbagai macam hambatan yang menyebabkan data hasil posisi menjadi tidak akurat. Selain kesalahan sistematik dari perangkat dan kesalahan yang sudah dihilangkan menggunakan mekanisme tertentu terdapat kesalahan yang seharusnya tidak muncul. Kesalahan ini akibat kekuranghatian pengolah data saat processing data satelit, penyebabnya mungkin bisa jadi kurang berhati-hati atau bahkan pengolah data kurang memiliki pemahaman terkait dengan metode pengolahan data terutama metode pengukuran tinggi alat (Height of Instrument). Kesalahan ini menyebabkan kualitas posisi yang dihasilkan berkurang, kesalahan yang sering terjadi ini dinamakan dengan blunder. Kebanyakan blunder bersumber pada metode yang digunakan untuk mengukur tinggi instrument, perlu diperhatikan juga bahwa pengolahan data sinyal oleh perangkat lunak dilakukan di Antenna Phase Center nya. Penelitian ini bertujuan untuk mengetahui efek blunder pengolahan data GPS terhadap hasil data posisi. Hasil penelitian menunjukkan bahwa blunder yang bersumber dari tidak ditentukannya tipe antenna akan mempengaruhi hasil koordinat tinggi sebesar nilai offset dari antenna tersebut yaitu pada penelitian ini sebesar 10 cm dari nilai sebenarnya. Kemudian untuk sumber kesalahan pengolahan dari tidak memasukkan nilai koordinat definitif yaitu pada penelitian ini memiliki kesalahan error sebesar 3,113 m untuk komponen tinggi dan 73 cm dan 32 cm untuk komponen horizontalnya. Dari hasil penelitian ini dapat disimpulkan bahwa kesalahan blunder pada pengolahan data satelit sangat mempengaruhi kualitas data posisi yang dihasilkan, terutama pada koordinat tingginya. ABSTRACTSatellite observations produce coordinate position based on the signals travel from the satellite to the antenna on Earth. On its journey, the satellite signal subjected to various kinds of barriers that cause data to become inaccurate positioning results. In addition to a systematic error of the device and the error has been eliminated using a specific mechanism there is an error that should not appear. This error is due to carelessness of data processor when processing satellite data, the cause might be less cautious or even lack an understanding of data processing associated with data processing methods particularly height measurement methods tool (Height of Instrument). This error causes the quality of the resulting position is reduced, a common mistake is called the error blunder. Most blunder rooted in the methods used to measure the height of the instrument, it should be noted that the data processing by software signal carried on its Antenna Phase Center. This study aimed to determine the effects of blunders on the results of data processing GPS position data. The results showed that the blunder derived from it determines the type of antenna will not affect the outcome of the high amount of the offset coordinates of the antenna is on the study of 10 cm from the actual value. Then to the source of the error of not entering the coordinate value that is definitive in this study had an error of 3.113 m for the vertical components and 73 cm and 32 cm for the horizontal component. From these results it can be concluded that the blunders in satellite data processing greatly affects the quality of the resulting position data, especially at the height coordinates.

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Voinov, A. V. "PyGPS: a GPS data processing automation package." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 6-8 (January 2001): 585–89. http://dx.doi.org/10.1016/s1464-1895(01)00105-3.

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Cahyadi, Mokhamad Nur, Ririn Wuri Rahayu, and Buldan Muslim. "Earthquake Monitoring Using Variometric GPS Data Processing." E3S Web of Conferences 94 (2019): 04007. http://dx.doi.org/10.1051/e3sconf/20199404007.

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Variometric Approach for Standalone Engine Displacement Analysis (VADASE) is a technique used in seismology purposes using GPS measurements. VADASE is used to determine the small displacement from the earthquake. The VADASE L1 solution is using the klobuchar ionospheric model. In this study VADASE was used in earthquakes with magnitudes> 7 to> 9 righter scales. In the scale of the earthquake category> 9 used Indian Ocean earthquake of December 26, 2016 with the strength of 9.1 SR by using the closest SAMP station and the Japanese Tohoku earthquake of March 11, 2011 with a power of 9.1 SR using 4 different stations namely MIZU, KMSV, TSK2 and Knii . The earthquake category with a scale of> 8 SR is the offshore earthquake Bio Bio, Chile on February 27, 2010 with a power of 8.8 SR using 2 stations namely ANTC and SANT, the Bengkulu Indonesia earthquake on 12 September 2007 with a power of 8.4 SR using the SAMP station, an illaper earthquake, chile September 16 2015 with 8.3 SR using SANT station, and Tres Piscos earthquake Mexico on September 8, 2017 with a power of 8.2 SR using IENG station. Earthquake with a strength of> 7 SR, namely the amberlay-New Zealand earthquake on November 13, 2016 with a strength of 7.8 SR using MRLL and WGTN stations, Puerto quello-chile earthquake on December 25, 2016 with a strength of 7.6 SR using COYQ station, Java sea earthquake -Indonesia on 8 August 2007 with 7.5 SR power using BAKO station and ayula mexico earthquake on 19 september 2017 with 7.1 SR power using INEG station. From the results of VADASE, the farthest distance from the epicenter to the observation station is 1100 km (INEG station) and the closest distance is 95 km (BAKO station). The highest speed is 0.12 m / s after 5 minutes from the earthquake in the earthquake Offshore Bio Bio-Chile 2010 uses the SANT station and the lowest speed is 0.006 m / s after 10 minutes from the earthquake in the 2007 Bengkulu earthquake using the SAMP station. Whereas in the other earthquakes was the 2011 Tohoku earthquake with a speed of 0.06 m / s after 1 minute using MIZU station, the amberley-New Zealand earthquake 2016 with a speed of 0.015 m / s after 1 minute using the MRLI satellite, Puerto quelloearthquake Chile 2016 with a speed of 0.025 m / s after 40 minutes using the COYQ satellite.

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Hein, Günter W., Alfred Leick, and Steven Lambert. "Integrated Processing of GPS and Gravity Data." Journal of Surveying Engineering 115, no. 1 (February 1989): 15–33. http://dx.doi.org/10.1061/(asce)0733-9453(1989)115:1(15).

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Xu, Guochang. "GPS data processing with equivalent observation equations." GPS Solutions 6, no. 1-2 (November 1, 2002): 28–33. http://dx.doi.org/10.1007/s10291-002-0009-3.

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Park, Joon-Kyu, Min-Gyu Kim, and Jong-Sin Lee. "Construction of Expert Service for GPS Relative Positioning Data Processing." Journal of the Korea Academia-Industrial cooperation Society 14, no. 5 (May 31, 2013): 2481–86. http://dx.doi.org/10.5762/kais.2013.14.5.2481.

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Bolotin, S., I. Gaiovitch, O. A. Khoda, A. Samoilenko, and Ya S. Yatskiv. "GPS Observational Campaign in the Geodynamics Test Area "SIMEIZ-KATSIVELI": Data Processing and Results." Kosmìčna nauka ì tehnologìâ 1, no. 2s (March 30, 1995): 3–16. http://dx.doi.org/10.15407/knit1995.02s.003.

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Huang, Wei Duo. "Research on Data Process of GPS in Road Surveying." Advanced Materials Research 433-440 (January 2012): 6007–13. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6007.

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In this paper, absorbing the domestic and foreign research results of GPS positioning technology, it is based on further GPS measurements in the highway engineering theory and methods in a systematic study of the GPS data processing process. It also introduces the high-precision GPS data processing software GAMIT. It also gives and analyzes the highway GPS data processing results from control measurements.

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Mohammed, Israa H., Tariq N. Ataiwe, and Hisham Al Sharaa. "Accuracy Assessment of a Variety of GPS Data Processing, Online Services and Software." Geomatics and Environmental Engineering 15, no. 4 (September 17, 2021): 5–19. http://dx.doi.org/10.7494/geom.2021.15.4.5.

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The processing of GPS observations in precise positioning is complex and requires professional surveyors since it must be carried out after each static measurement. In GPS network adjustment, the obtaining of the correct coordinates of the determined point is possible after determining the components of GPS vectors and aligning the networks of these vectors, while PPP requires the availability of precise products for the reference satellites orbits and clock. For that reason, surveyors can take advantage of free online GPS data processing. In this paper, the authors compare the results obtained from different sources of free online GPS data processing (AUSPOS, OPUS, CenterPoint RTX, APPS, MagicGNSS, CSRS-PPP, GAPS, and SCOUT) in terms of their accuracy, availability, and operation. This is then compared with free GPS processing software (gLAB and RTKLIB), and finally with commercial software (TBC Trimble Business Center). The results show that online processing services are more accurate than offline processing software, which indicates the strength of their algorithms and processes. The CSRS-PPP online service had the best results. The difference between the relative solution of AUSPOS and OPUS, and CSRS-PPP is insignificant.

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李, 敏. "Discussion on Teaching of “GPS Measurement and Data Processing”." Geomatics Science and Technology 02, no. 01 (2014): 6–8. http://dx.doi.org/10.12677/gst.2014.21002.

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耿, 涛. "Discussions on Teaching of “GPS Surveying and Data Processing”." Advances in Education 07, no. 06 (2017): 372–75. http://dx.doi.org/10.12677/ae.2017.76058.

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King, Matt. "Rigorous GPS data-processing strategies for glaciological applications." Journal of Glaciology 50, no. 171 (2004): 601–7. http://dx.doi.org/10.3189/172756504781829747.

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AbstractGlobal positioning system (GPS) data are now routinely used for many glaciological applications. In some common cases, systematic errors are unmodelled at the data-processing stage, although they are often presumed insignificant. In this paper, I investigate these assumptions for three different scenarios: (1) measurements on a moving glacier; (2) measurements on a floating ice shelf; and (3) precise height determination over large elevation ranges, such as for aircraft positioning in lidar/laser altimeter missions. In each case, systematic errors are shown to be present in the coordinate solutions that have a far greater magnitude than the formal error estimates produced by the GPS processing software, under certain conditions. If these coordinate biases go undetected, short- and long-term measurements of horizontal ice velocity or rates of ice-thickness change may be erroneous and the coordinates could not be expected to match rigorously processed data or results from different processing techniques. More rigorous processing strategies are discussed that allow for bias-free parameter estimation.

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Wang, Jinling, Mike P. Stewart, and Maria Tsakiri. "Stochastic Modeling for Static GPS Baseline Data Processing." Journal of Surveying Engineering 124, no. 4 (November 1998): 171–81. http://dx.doi.org/10.1061/(asce)0733-9453(1998)124:4(171).

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Liu, Lilong, Hongyan Wen, and Bin Liu. "Comparison of models for GPS kinematic data processing." Geo-spatial Information Science 11, no. 2 (January 2008): 152–56. http://dx.doi.org/10.1007/s11806-008-0049-0.

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Stoew, B., P. Jarlemark, J. Johansson, and G. Elgered. "Real-time processing of GPS data delivered by SWEPOS." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 26, no. 6-8 (January 2001): 493–96. http://dx.doi.org/10.1016/s1464-1895(01)00090-4.

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Hepner, S. A. R., L. L. Bagnasehi, and H. P. Geering. "Robust Estimation with Application to Processing of GPS-data." IFAC Proceedings Volumes 26, no. 2 (July 1993): 783–87. http://dx.doi.org/10.1016/s1474-6670(17)48838-0.

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Wu, S. C., and W. G. Melbourne. "An optimal GPS data processing technique for precise positioning." IEEE Transactions on Geoscience and Remote Sensing 31, no. 1 (1993): 146–52. http://dx.doi.org/10.1109/36.210455.

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Won, Ji-Hye, Kwan-Dong Park, Ji-Hyun Ha, and Jung-Ho Cho. "Effects of Tropospheric Mapping Functions on GPS Data Processing." Journal of Astronomy and Space Sciences 27, no. 1 (March 15, 2010): 21–30. http://dx.doi.org/10.5140/jass.2010.27.1.021.

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Rideout, William, and Anthea Coster. "Automated GPS processing for global total electron content data." GPS Solutions 10, no. 3 (May 11, 2006): 219–28. http://dx.doi.org/10.1007/s10291-006-0029-5.

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Yalova, K., K. Yashyna, and O. Tarasiyk. "AUTOMATED INFORMA¬TION SYSTEM FOR GPS MONITORING DATA PROCESSING." Collection of scholarly papers of Dniprovsk State Technical University (Technical Sciences) 2, no. 37 (April 23, 2021): 88–92. http://dx.doi.org/10.31319/2519-2884.37.2020.16.

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Using of automated information systems in the field of geolocation data processing increases the control and management efficiency of freight and passenger traffic. The article presents the results of design and software implementation of the automated information system that allows monitoring of GPS tracking data in real time, build routes and set control points for it, generate system messages about the status of vehicles on the route and generate reporting information on the base of user requests. The design of the system architecture and interface was carried out on the basis of developed object and functional data domain models, which take into account its structural and functional features. The microservice approach principles were applied during the developing of the system architecture. The system software is a set of independent services that work in their own process, implement a certain business logic algorithm and communicate with other services through the HTTP protocol. The set of the system software services consists of: a service for working with GPS data, a service for implementing geolocation data processing functions, and a web application service. The main algorithms of the developed system services and their functional features are described in the work. Article’s figures graphically describe developed system site map and system typical Web forms. This data displays the composition of web pages, paths between them and shows the user interface. The design of the user interface was carried out taking into account quality requirements of user graphical web interfaces.

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Voronov, G. A. "Comparison and analysis of GLONASS and GPS data post-processing results." MINING INFORMATIONAL AND ANALYTICAL BULLETIN 9 (2018): 111–17. http://dx.doi.org/10.25018/0236-1493-2018-9-0-111-117.

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He, Fu Xiang, Li Hong Ren, and Yong Sheng Ding. "Intelligent Clothing Embedded GPS Data Processing Based on Shifting Huffman Coding." Applied Mechanics and Materials 195-196 (August 2012): 1116–21. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1116.

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According to the characteristic of GPS system fo intelligent garment, this paper presents a strategy of GPS data processing by removing redundant information and shifting the combination codes. Its compressed result is analyzed by comparing with direct data compression and Huffman coding. The above-mentioned strategy takes into account both the speed and the effect of the compression, and has a good performance in the compression rate. The non-destructive GPS data processing method has a positive and practical significance in the intelligent garment for efficient monitoring.

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Hsu, Po Hsien, Lie Chung Shen, Ching Liang Tseng, Jaw Fang Lee, and Ding Yu Liu. "Using MATLAB for Processing RGPS Data to Determine the Altitude of Water Surface." Applied Mechanics and Materials 311 (February 2013): 67–72. http://dx.doi.org/10.4028/www.scientific.net/amm.311.67.

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The purpose of this study is to use the software of MATLAB for processing the direct and reflected signals of GPS to monitor altitude of water surface in the water flume, as well as to establish a practicable technique of measuring sea water level. It is because before determining water wave pattern, the feasibility and accuracy of reflected GPS method must be proved. Therefore, there was a field test in the Mid-size wave flume of the Tainan Hydraulics Laboratory, National Cheng Kung University. After improving RGPS positioning procedure, the tranquil water level and the steadily descending of water level observation for one hour were performed. In this research, an integrated GPS receiver that employed direct and reflected GPS signals for the measurement was introduced. Both RHCP and LHCP antennas were employed to simultaneously receive the L1 and L2 carrier phase of direct and reflected signals. After the data analysis of the GPS observation, the position of signal reflection and the water level of the wave were solved. The results of the RGPS, wave gauge, and staff meter were coincided within 0.5cm ~ 1.0 cm. The differences between the reflection heights of this GPS system and the record of wave gauge were almost identical within 90%. It is proved the reflected GPS technique is possible to monitor water surface altitude and determine water wave patterns.

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Kwon, H., J. S. Kang, Y. Jo, and J. H. Kang. "Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system." Atmospheric Measurement Techniques 8, no. 3 (March 16, 2015): 1259–73. http://dx.doi.org/10.5194/amt-8-1259-2015.

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Abstract. The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS package for observation processing (KPOP) system for data assimilation, preprocessing, and quality control modules for bending-angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. The GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending-angle operator, and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS local ensemble transform Kalman filter (LETKF) data assimilation system, which has been successfully implemented to a cubed-sphere model with unstructured quadrilateral meshes. As a result of data processing, the bending-angle departure statistics between observation and background show significant improvement. Also, the first experiment in assimilating GPS-RO bending angle from KPOP within KIAPS-LETKF shows encouraging results.

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Kwon, H., J. S. Kang, Y. Jo, and J. H. Kang. "Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system." Atmospheric Measurement Techniques Discussions 7, no. 11 (November 28, 2014): 11927–56. http://dx.doi.org/10.5194/amtd-7-11927-2014.

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Abstract. The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS Package for Observation Processing (KPOP) system for data assimilation, preprocessing and quality control modules for bending angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending angle operator and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model-Spectral Element (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS-LETKF data assimilation system, which has been successfully implemented to a cubed-sphere model with fully unstructured quadrilateral meshes. As a result of data processing, the bending angle departure statistics between observation and background shows significant improvement. Also, the first experiment in assimilating GPS-RO bending angle resulting from KPOP within KIAPS-LETKF shows encouraging results.

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He, Eric, Fan Bai, Curtis Hay, Jinzhu Chen, and Vijayakumar Bhagavatula. "A Map Inference Approach Using Signal Processing from Crowd-sourced GPS Data." ACM Transactions on Spatial Algorithms and Systems 7, no. 2 (February 2021): 1–23. http://dx.doi.org/10.1145/3431785.

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The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. We explore map inference using a three-stage approach, which incorporates a novel Multi-source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first use of estimation theory for map inference. In particular, our approach addresses the unique challenges of vehicular GPS data. This data is plentiful but suffers from noise in location and variable coverage of regions. This makes it difficult to differentiate between noise and sparsely covered regions when increasing coverage and reducing noise. Due to the asynchronous, variable sampling rate, and often under-sampled nature of the data, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. We evaluated our method for map inference by comparing to Open Street Map maps as ground truth. We use the F-Measure, Precision, and Recall metrics to evaluate our method on Tsinghua University’s Beijing Taxi Dataset and Shanghai Jiao Tong University’s SUVnet Dataset. On these datasets, we obtained a mean<?brk?> F-Measure, Precision, and Recall of 0.7212, 0.9165, and 0.6021, respectively, outperforming a well-known method based on Kernel Density Estimation in terms of these evaluation metrics.

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Li, Xiao Yong, Zhong Hua Zhang, Wei Kang Zhu, Yuan Xin Qu, Gui Ming Chen, and Lei Yang. "GPS Data Processing Models for Maritime Dynamic Precision Evaluation of Shipboard Tracking Equipments." Advanced Materials Research 466-467 (February 2012): 1070–74. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1070.

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To get a better understanding of tracking capability and accuracy of the equipments onboard space tracking ships, it is necessary to perform precision evaluation. In order to achieve satisfactory results, dynamic flight experiment is organized, during which a series of new methods and technologies are applied, with GPS carrier wave phase difference technology as the center, among which GPS data processing technology is empathized. Based on above, a set of complete GPS data processing models for precision evaluation are established

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Lipar, Peter, Irena Strnad, Martin Česnik, and Tomaž Maher. "Development of Urban Driving Cycle with GPS Data Post Processing." PROMET - Traffic&Transportation 28, no. 4 (August 30, 2016): 353–64. http://dx.doi.org/10.7307/ptt.v28i4.1916.

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This paper presents GIS-based methodology for urban area driving cycle construction. The approach reaches beyond the frames of usual driving cycle development methods and takes into account another perspective of data collection. Rather than planning data collection, the approach is based on available in-vehicle measurement data post processing using Geographic Information Systems to manipulate the excessive database and extract only the representative and geographically limited individual trip data. With such data post processing the data was carefully adjusted to include only the data that describe representative driving in Ljubljana urban area. The selected method for the driving cycle development is based on searching for the best microtrips combination while minimizing the difference between two vectors; one based on generated cycle and the other on the database. Accounting for a large random sample of actual trip data, our approach enables more representative area-specific driving cycle development than the previously used techniques.

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Jarlemark, Per O. J., Jan M. Johansson, Borys Stoew, and Gunnar Elgered. "Real time GPS data processing for regional atmospheric delay derivation." Geophysical Research Letters 29, no. 16 (August 15, 2002): 7–1. http://dx.doi.org/10.1029/2001gl014568.

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Huang, S.-Q., and J.-X. Wang. "New data processing strategy for single frequency GPS deformation monitoring." Survey Review 47, no. 344 (October 8, 2014): 379–85. http://dx.doi.org/10.1179/1752270614y.0000000138.

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Prabha, R., and Mohan G. Kabadi. "A comprehensive insight towards Pre-processing Methodologies applied on GPS data." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2742. http://dx.doi.org/10.11591/ijece.v10i3.pp2742-2754.

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods.

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MOSAVI, M. R. "GPS RECEIVERS TIMING DATA PROCESSING USING NEURAL NETWORKS: OPTIMAL ESTIMATION AND ERRORS MODELING." International Journal of Neural Systems 17, no. 05 (October 2007): 383–93. http://dx.doi.org/10.1142/s0129065707001226.

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The Global Positioning System (GPS) is a network of satellites, whose original purpose was to provide accurate navigation, guidance, and time transfer to military users. The past decade has also seen rapid concurrent growth in civilian GPS applications, including farming, mining, surveying, marine, and outdoor recreation. One of the most significant of these civilian applications is commercial aviation. A stand-alone civilian user enjoys an accuracy of 100 meters and 300 nanoseconds, 25 meters and 200 nanoseconds, before and after Selective Availability (SA) was turned off. In some applications, high accuracy is required. In this paper, five Neural Networks (NNs) are proposed for acceptable noise reduction of GPS receivers timing data. The paper uses from an actual data collection for evaluating the performance of the methods. An experimental test setup is designed and implemented for this purpose. The obtained experimental results from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of methods to give high accurate timing. Quality of the obtained results is very good, so that GPS timing RMS error reduce to less than 120 and 40 nanoseconds, with and without SA.

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Yuan, Yuan, Zhi Yong Liu, Huai Kun Xiang, and Ze Feng Ding. "Traffic Survey System Based on GPS, GPRS and GIS." Advanced Materials Research 328-330 (September 2011): 989–97. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.989.

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Referring to research findings of existing traffic survey instrument, this paper bases on electronic traffic survey counter to insert GPRS module, micro-program controller, vehicle type selection keyboard and direction selection keyboard, and utilizes computer technology to establish data processing platform, studies and designs the traffic survey system on the basis of GPS, GPRS and GIS. This system can realize real-time monitoring on surveyors, accurate and timely transmission and processing of survey data, and automatic analysis and management of survey data.

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Zhang, Jun. "Study on the SVM Processing Model of the GPS Monitoring Data of Coal Mine Subsidence." Applied Mechanics and Materials 598 (July 2014): 436–41. http://dx.doi.org/10.4028/www.scientific.net/amm.598.436.

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In order to make the GPS monitoring data of coal mine subsidence useful and effective in engineering practice, this paper tries to analyze the exceptional handling processing model of the GPS monitoring data of coal mine subsidence under the guidance of the principle of support vector machine (SVM) regression, its calculating method and the application of regression program produced by MATLAB. By comparing the result of the exceptional handling processing model established on practical measured data with the one of the polynomial function fitting, this thesis proves that the application of vector regression algorithm in studies on the exceptional handling processing model of the GPS monitoring data is highly effective.

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Lee, Woo-Kyoung, Jong-Kyun Chun, Sung-Ki Cho, Jong-Uk Park, Jung-Ho Cho, Jae-Cheol Yoon, Jin-Ho Lee, Yong-Sik Chun, and Sang-Ryul Lee. "RETRIEVAL OF ELECTRON DENSITY PROFILE FOR KOMPSAT-5 GPS RADIO OCCULTATION DATA PROCESSING SYSTEM." Journal of Astronomy and Space Sciences 24, no. 4 (December 15, 2007): 297–308. http://dx.doi.org/10.5140/jass.2007.24.4.297.

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Lytvyn, M. O. "Comparison between results of Ukrainian permanent GPS-network data processing with GAMIT/GLOBK and MAO GPS Local analysis centre results." Kosmìčna nauka ì tehnologìâ 11, no. 5-6 (November 30, 2005): 56–63. http://dx.doi.org/10.15407/knit2005.05.056.

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Rapinski, Jacek, Slawomir Cellmer, and Zofia Rzepecka. "Modified GPS/Pseudolite Navigation Message." Journal of Navigation 65, no. 4 (March 23, 2012): 711–16. http://dx.doi.org/10.1017/s0373463312000124.

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One of the issues regarding integrated GPS/pseudolite measurements is how to deliver a pseudolite's position to a receiver or to post-processing software and how to manage it. This paper presents a proposed solution to this problem. The standard navigation message is modified in such way that without changing receivers (or post-processing software), the calculated position of a transmitter is fixed at a pseudolite's known position. The formulae for modification of standard Ephemeris Data are also derived. This algorithm can be implemented in a transmitter's firmware or a navigation data file can be modified for post-processing.

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Cui, Baojian, and Yuzhen Wang. "Study on data processing method of GPS disciplined rubidium frequency standard." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 24, no. 9 (November 30, 2010): 808–13. http://dx.doi.org/10.3724/sp.j.1187.2010.00808.

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Wickert, Jens, Roman Galas, Torsten Schmidt, Georg Beyerle, Christoph Reigber, Christoph Förste, and Markus Ramatschi. "Atmospheric sounding with CHAMP: GPS ground station data for occultation processing." Physics and Chemistry of the Earth, Parts A/B/C 29, no. 2-3 (January 2004): 267–75. http://dx.doi.org/10.1016/j.pce.2004.01.015.

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S. T. Drummond, C. W. Fraisse, and K. A. Sudduth. "COMBINE HARVEST AREA DETERMINATION BY VECTOR PROCESSING OF GPS POSITION DATA." Transactions of the ASAE 42, no. 5 (1999): 1221–28. http://dx.doi.org/10.13031/2013.13287.

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Wang, Ershen, Tao Pang, and Yongming Yang. "Research on GPS Receiver Data Processing Algorithm Based on Wavelet Analysis." International Journal of Hybrid Information Technology 8, no. 11 (November 30, 2015): 405–12. http://dx.doi.org/10.14257/ijhit.2015.8.11.36.

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Fornaro, Gianfranco, Nicola D'Agostino, Roberta Giuliani, Carlo Noviello, Diego Reale, and Simona Verde. "Assimilation of GPS-Derived Atmospheric Propagation Delay in DInSAR Data Processing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 2 (February 2015): 784–99. http://dx.doi.org/10.1109/jstars.2014.2364683.

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Procházka, Aleš, Saeed Vaseghi, Mohammadreza Yadollahi, Ondřej Ťupa, Jan Mareš, and Oldřich Vyšata. "Remote physiological and GPS data processing in evaluation of physical activities." Medical & Biological Engineering & Computing 52, no. 4 (December 24, 2013): 301–8. http://dx.doi.org/10.1007/s11517-013-1134-6.

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Chan, W. S., Y. L. Xu, X. L. Ding, and W. J. Dai. "An integrated GPS–accelerometer data processing technique for structural deformation monitoring." Journal of Geodesy 80, no. 12 (September 7, 2006): 705–19. http://dx.doi.org/10.1007/s00190-006-0092-2.

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You, Soyoung Iris, and Stephen G. Ritchie. "A GPS Data Processing Framework for Analysis of Drayage Truck Tours." KSCE Journal of Civil Engineering 22, no. 4 (April 2018): 1454–65. http://dx.doi.org/10.1007/s12205-017-0160-6.

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Journal articles on the topic 'GPS data processing'

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