Relevant bibliographies by topics / GPS data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'GPS data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "GPS data processing"

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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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<p class="judulabstrakindo"><strong><span lang="IN"> ABSTRAK</span></strong></p><p class="abstrakindo"><span lang="IN">Pengamatan 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 (<em>Height of Instrument</em>). Kesalahan ini menyebabkan kualitas posisi yang dihasilkan berkurang, kesalahan yang sering terjadi ini dinamakan dengan <em>blunder</em></span>.<span lang="IN"> Kebanyakan blunder bersumber pada metode yang digunakan untuk mengukur tinggi instrument, perlu diperhatikan juga bahwa pengolahan data sinyal oleh perangkat lunak dilakukan di <em>Antenna Phase Center</em> 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 <em>offset</em> dari antenna tersebut yaitu pada penelitian ini sebesar 10 cm dari nilai sebenarnya. Kemudian untuk sumber kesalahan</span> pengolahan<span lang="IN"> 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 <em>blunder</em> pada pengolahan data satelit sangat mempengaruhi kualitas data posisi yang dihasilkan, terutama pada koordinat tingginya.</span></p><p class="abstrakindo"><span lang="IN"><br /></span></p><p class="judulabstraking"><strong><span lang="IN"> ABSTRACT</span></strong></p><p class="abstraking"><span lang="EN-GB">Satellite 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.</span></p>
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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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Dissertations / Theses on the topic "GPS data processing"

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Griffin, Terry W. "GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115089/.

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In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and make it highly usable for the creation of prediction algorithms; (3) creation of prediction/labeling algorithms at such a level that they are viable for commercial use. This study identifies the key research problems toward building the CaPPture (collection, processing, prediction) system.
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Hart, Dennis L., Johnny J. Pappas, and John E. Lindegren. "Desktop GPS Analyst Standardized GPS Data Processing and Analysis on a Personal Computer." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611424.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
In the last few years there has been a proliferation of GPS receivers and receiver manufacturers. Couple this with a growing number of DoD test programs requiring high accuracy Time-Space-Position-Information (TSPI) with diminishing test support funds and/or needing a wide area, low altitude or surface tracking capability. The Air Force Development Test Center (AFDTC) recognized the growing requirements for using GPS in test programs and the need for a low cost, portable TSPI processing capability which sparked the development of the Desktop GPS Analyst. The Desktop GPS Analyst is a personal computer (PC) based software application for the generation of GPS-based TSPI.
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Satirapod, Chalermchon Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "Improving the GPS Data Processing Algorithm for Precise Static Relative Positioning." Awarded by:University of New South Wales. School of Surveying and Spatial Information Systems, 2002. http://handle.unsw.edu.au/1959.4/18244.

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Since its introduction in the early 1980????s, the Global Positioning System (GPS) has become an important tool for high-precision surveying and geodetic applications. Carrier phase measurements are the key to achieving high accuracy positioning results. This research addresses one of the most challenging aspects in the GPS data processing algorithm, especially for precise GPS static positioning, namely the definition of a realistic stochastic model. Major contributions of this research are: (a) A comparison of the two data quality indicators, which are widely used to assist in the definition of the stochastic model for GPS observations, has been carried out. Based on the results obtained from a series of tests, both the satellite elevation angle and the signal-to-noise ratio information do not always reflect the reality. (b) A simplified MINQUE procedure for the estimation of the variance-covariance components of GPS observations has been proposed. The proposed procedure has been shown to produce similar results to those from the standard MINQUE procedure. However, the computational load and time are significantly reduced, and in addition the effect of a changing number of satellites on the computations is effectively dealt with. (c) An iterative stochastic modelling procedure has been developed in which all error features in the GPS observations are taken into account. Experimental results show that by applying the proposed procedure, both the certainty and the accuracy of the positioning results are improved. In addition, the quality of ambiguity resolution can be more realistically evaluated. (d) A segmented stochastic modelling procedure has been developed to effectively deal with long observation period data sets, and to reduce the computational load. This procedure will also take into account the temporal correlations in the GPS measurements. Test results obtained from both simulated and real data sets indicate that the proposed procedure can improve the accuracy of the positioning results to the millimetre level. (e) A novel approach to GPS analysis based on a combination of the wavelet decomposition technique and the simplified MINQUE procedure has been proposed. With this new approach, the certainty of ambiguity resolution and the accuracy of the positioning results are improved.
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Zhao, Xiaoyun. "Road network and GPS tracking with data processing and quality assessment." Licentiate thesis, Högskolan Dalarna, Mikrodataanalys, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:du-17354.

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GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.
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Wolf, Jean Louise. "Using GPS data loggers to replace travel diaries in the collection of travel data." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/20203.

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Sun, Bingxia. "Identifying activity type and trip prupose from data collected by passive GPS." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1385.

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Zimba, Robert. "High precision GPS data processing for the survey of South African tide gauges." Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/4977.

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King, Peter Haywood. "A Low Cost Localization Solution Using a Kalman Filter for Data Fusion." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32384.

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Position in the environment is essential in any autonomous system. As increased accuracy is required, the costs escalate accordingly. This paper presents a simple way to systematically integrate sensory data to provide a drivable and accurate position solution at a low cost. The data fusion is handled by a Kalman filter tracking five states and an undetermined number of asynchronous measurements. This implementation allows the user to define additional adjustments to improve the overall behavior of the filter. The filter is tested using a suite of inexpensive sensors and then compared to a differential GPS position. The output of the filter is indeed a drivable solution that tracks the reference position remarkably well. This approach takes advantage of the short-term accuracy of odometry measurements and the long-term fix of a GPS unit. A maximum error of two meters of deviation from the reference is shown for a complex path over two minutes and 100 meters long.
Master of Science
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Gentek, Anna. "Activity Recognition Using Supervised Machine Learning and GPS Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295600.

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Human Activity Recognition has become a popular research topic among data scientists. Over the years, multiple studies regarding humans and their daily motion habits have been investigated for many different purposes. This fact is not surprising when we look at all the opportunities and applications that can be applied and utilized thanks to the results of these algorithms. In this project we implement a system that can effectively collect sensor data from mobile devices, process it and by using supervised machine learning successfully predict the class of a performed activity. The project was executed based on datasets and features extracted from GPS sensors. The system was trained using various machine learning algorithms and Python SciKit to guarantee optimal solutions with accurate predictions. Finally, we applied a majority vote rule to secure the best possible accuracy of the activity classification process. As a result we were able to identify various activities including walking, cycling, driving and public transportation methods bus and metro with 90+% accuracy.
Att utföra aktivitetsigenkänning på människor har blivit ett populärt forskningsämne bland datavetare, där flertalet studier rörande människor och deras dagliga rörelsevanor undersökts för många olika syften. Detta är inte förvånande när man ser till de möjligheter och användningsområden som kan tillämpas och utnyttjas tack vare resultaten från dessa system. Detta projekt går ut på att implementera ett system som mha samlad sensordata från mobila enheter, kan bearbeta den och genom s.k övervakad maskininlärning med goda resultat bestämma den aktivitet som utförts. Projektet genomfördes baserat på dataset och egenskaper extraherade från GPS-data. Systemet tränades med olika maskininlärningsalgoritmer genom Python SciKit för att välja den bäst lämpade metoden för detta projekt. Slutligen tillämpade vi majority votemetoden för att säkerställa bästa möjliga noggrannhet i aktivitetsklassificeringsprocessen. Resultatet blev ett system som framgångsrikt kan identifiera aktiviteterna gå, cykla, köra bil samt med ett ytterligare fokus på kollektivtrafikmetoderna buss och tunnelbana, med en noggrannhet på över 90%.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Grejner-Brzezinska, Dorota A. "Analysis of GPS Data Processing Techniques: In Search of Optimized Strategy of Orbit and Earth Rotation Parameter Recovery /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487929745335624.

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Books on the topic "GPS data processing"

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C. C. J. M. Tiberius. Recursive data processing for kinematic GPS surveying. Delft: Nederlandse Commissie Voor Geodesie, 1998.

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Sahoo, Rabi N. Practical manual on basics of remote sensing data processing, GPS and GIS. New Delhi: Division of Agricultural Physics, Indian Agricultural Research Institute, 2012.

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GPS Guan ce shu ju chu li yu ying yong. Beijing: Ke xue chu ban she, 2012.

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Frei, Erwin. Rapid differential positioning with the Global Positioning System (GPS). [Zürich]: Schweizerischen Geodätischen Kommission, 1991.

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Chen, Dewang, and Ruijun Cheng. Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0.

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Amiri-Simkooei, AliReza. Least-squares variance component estimation: Theory and GPS applications. Delft: NCG, Nederlandse Commissie voor Geodesie, 2007.

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Faechner, Ty. Evaluation of GPS yield mapping technology at reclaimed industrial sites in Alberta. Edmonton: Alberta Environment, 2006.

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R, Kumar. Efficient detection and signal parameter estimation with applications to high dynamic GPS receivers. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1989.

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Sluiter, P. G. Geodetic dual-frequency GPS receivers under anti-spoofing: Comparison of four receivers for baseline accuracy susceptibility to radio frequency interference noise in the observables. Delft, the Netherlands: Netherlands Geodetic Commission, 1995.

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Ishikawa, Jun. Gazō GPS tō no sensa tōgō ni yoru nichijō riyō kanō na okunaigai shikaku shōgaisha hokō shien shisutemu no kaihatsu ni kansuru kenkyū: Shōgaisha jiritsu shien kiki tō kenkyū kaihatsu purojekuto : Heisei 21-nendo sōkatsu buntan kenkyū hōkokusho. [Shizuoka-shi: Ishikawa Jun?], 2010.

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Book chapters on the topic "GPS data processing"

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Xu, Guochang, and Yan Xu. "Parameterisation and Algorithms of GPS Data Processing." In GPS, 263–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50367-6_9.

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Luo, Xiaoguang. "Data and GPS Processing Strategies." In GPS Stochastic Modelling, 117–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5_4.

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Blewitt, Geoffrey. "GPS Data Processing Methodology: from Theory to Applications." In GPS for Geodesy, 231–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72011-6_6.

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Napier, Michael. "Data Processing for GPS/INS Integration." In High Precision Navigation, 571–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74585-0_41.

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Ao, Chi O., George A. Hajj, Thomas K. Meehan, Stephen S. Leroy, E. Robert Kursinski, Manuel Torre de la Juárez, Byron A. Iijima, and Anthony J. Mannucci. "Backpropagation Processing of GPS Radio Occultation Data." In First CHAMP Mission Results for Gravity, Magnetic and Atmospheric Studies, 415–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-38366-6_57.

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Betti, B., M. Crespi, B. Marana, and G. Venuti. "A New Software for GPS Data Processing." In GPS Trends in Precise Terrestrial, Airborne, and Spaceborne Applications, 320–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80133-4_53.

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Torp, Kristian, Ove Andersen, and Christian Thomsen. "Travel-Time Computation Based on GPS Data." In Lecture Notes in Business Information Processing, 70–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61627-4_4.

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Wanninger, Lambert, and Cord-Hinrich Jahn. "Effects of Severe Ionospheric Conditions on GPS Data Processing." In International Association of Geodesy Symposia, 141–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77726-4_13.

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Chen, Dewang, and Ruijun Cheng. "Data Reduction." In Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway, 103–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0_6.

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Chen, Dewang, and Ruijun Cheng. "Multiple GPS Track Information Fusion." In Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway, 117–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0_7.

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Conference papers on the topic "GPS data processing"

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Freitas, Tiago R. M., Antoanio Coelho, and Rosaldo J. F. Rossetti. "Improving digital maps through GPS data processing." In 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2009. http://dx.doi.org/10.1109/itsc.2009.5309537.

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Freitas, Tiago R. M., Antonio Coelho, and Rosaldo J. F. Rossetti. "Correcting routing information through GPS data processing." In 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsc.2010.5624996.

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Chen, Gang, and Xiong Pan. "Estimation methods for GPS kinematic data processing." In International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), edited by Honghua Tan and Qi Luo. SPIE, 2009. http://dx.doi.org/10.1117/12.836652.

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Wang Lijun, Yang Xiaoniu, and Zhao Huichang. "Data acquisition and processing technique for software GPS receivers." In 2008 International Conference on Microwave and Millimeter Wave Technology (ICMMT). IEEE, 2008. http://dx.doi.org/10.1109/icmmt.2008.4540880.

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Lilong, Liu, Wen Hongyan, and Liu Bin. "An Investigation of Models for GPS Kinematical Data Processing." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.219.

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Li, Deying, Kunlong Yin, Lixia Chen, and Wenming Chen. "Application of GPS monitoring data in landslide prediction." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.811371.

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Rao, S. Aishwarya, Sahana S, Priyanka N S, Inchara G, and Rajshekar M B. "GPS Based Toll Collection System." In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. AIJR Publisher, 2018. http://dx.doi.org/10.21467/proceedings.1.18.

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Zhijian Wang, Zhengxi Li, and Li wang. "The application of GPS data processing technique in map matching." In 2011 International Conference on Transportation and Mechanical & Electrical Engineering (TMEE). IEEE, 2011. http://dx.doi.org/10.1109/tmee.2011.6199272.

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Xiaoqi Lan, Sen Li, and Kun Xie. "The data processing model of monitoring crustal deformation using GPS." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5964412.

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Lou, Yidong, Chuang Shi, Maorong Ge, and Shirong Ye. "Determination of GPS real-time precise clock offset." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.816366.

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Reports on the topic "GPS data processing"

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Morley, Steven, John Sullivan, Richard Schirato, and James Terry. Data Processing for Energetic Particle Measurements from the Global Positioning System (GPS) constellation. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164428.

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Moses, Adam J. CT-Analyst GIS Data Processing Guidance Document. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada603308.

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R. Will Grimes, Norman Merriam, L.J. Fahy, C.G. Mones, Jr L.A. Johnson, F.M. Carlson, T.F. Turner, et al. 1.PRELIMINARY EVALUATION OF A PROCESS USING PLASMA REACTIONS TO DESULFURIZE HEAVY OILS; 2.PROCESS SUPPORT AND DEVELOPMENT FOR COMPCOAL; 3.MISCIBLE/IMMISCIBLE GAS INJECTION PROCESSES; 4.COMPCOAL: A PROFITABLE PROCESS FOR PRODUCTION OF A STABLE HIGH-BTU FUEL FROM POWDER RIVER BASIN COAL; 5.EVALUATION OF ALTERNATE FREE RADICAL INITIATORS FOR HEAVY OIL/PLASTICS CO-PROCESSING; 6.DEVELOPMENT OF AN ON-LINE ALKALI MONITORING PROBE; 7.DEVELOPMENT OF A PORTABLE DATA ACQUISITION SYSTEM; 8.BENCH-SCALE TESTING AND VERIFICATION OF PYROLYSIS CONCEPT FOR REMEDIATION OF TANK BOTTOMS; 9.HAZ-FLOTE: EX-SITU DECONTAMINATION OF MATERIALS; 10.IN-SITU AMELIORATION OF ACID MINE DRAINAGE PROBLEMS; 11.THE SYNAG PROCESS: COAL COMBUSTION ASH MANAGEMENT OPTION; 12.CONDITIONING AND HYDRATION REACTIONS ASSOCIATED WITH CLEAN COAL TECHNOLOGY ASH DISPOSAL/HYDRATION. Office of Scientific and Technical Information (OSTI), October 1999. http://dx.doi.org/10.2172/767235.

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