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Titlebook: GPS Stochastic Modelling; Signal Quality Measu Xiaoguang Luo Book 2013 Springer-Verlag Berlin Heidelberg 2013 ARMA Process.AutoRegressive M

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發(fā)表于 2025-3-21 19:31:06 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱GPS Stochastic Modelling
副標題Signal Quality Measu
編輯Xiaoguang Luo
視頻videohttp://file.papertrans.cn/381/380158/380158.mp4
概述Outstanding doctoral thesis nominated for a Springer Theses Prize by Karlsruhe Institute of Technology, Germany.This work is a key step towards a realistic GNSS stochastic model, and provides good exa
叢書名稱Springer Theses
圖書封面Titlebook: GPS Stochastic Modelling; Signal Quality Measu Xiaoguang Luo Book 2013 Springer-Verlag Berlin Heidelberg 2013 ARMA Process.AutoRegressive M
描述Global Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates. .This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of GPS data analysis, while the temporal correlation of GPS observation noise can be efficiently described by means of autoregressive moving average (ARMA) processes. Furthermore, this work includes an up-to-date overview of the GNSS error effects and a comprehensive description of various mathematical methods. .
出版日期Book 2013
關鍵詞ARMA Process; AutoRegressive Moving Average Process; Hypothesis Testing; Signal-to-Noise Ratio (SNR); St
版次1
doihttps://doi.org/10.1007/978-3-642-34836-5
isbn_softcover978-3-662-51188-6
isbn_ebook978-3-642-34836-5Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

書目名稱GPS Stochastic Modelling影響因子(影響力)




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沙發(fā)
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Data and GPS Processing Strategies,ghting and the residual-based temporal correlation modelling, which will be presented in Chaps.?5 and 7, respectively. In addition to representative GPS measurements, freely available surface meteorological data are incorporated, enabling a physically reasonable interpretation of the results. Sectio
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Results of SNR-Based Observation Weighting, EMPSNR2 on GPS baseline solutions using the Bernese GPS Software?5.0. Thereby, three important aspects, namely ambiguity resolution, troposphere parameter estimation and coordinate determination, are taken into account. Being structured in a similar way, Sects.?6.1 and 6.2 first compare the weight
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Conclusions and Recommendations,ter estimates and realistic quality measures. In comparison to the highly developed functional model, the stochastic model applied in many GPS software products is considered unrealistic due to the elevation-dependent (or even identical) weighting model and the neglect of physical correlations betwe
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