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Image of - GPS Stochastic Modelling | Hardcover
GPS Stochastic Modelling | Hardcover

GPS Stochastic Modelling | Hardcover

by Luo

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.

Highlights

  • binding-icon

    9783642348358

    ISBN:

  • binding-icon

    Luo

    Author:

  • binding-icon

    331

    Pages:

  • binding-icon

    215 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2013

    Year:

  • edition-icon

    N/A

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹16167

₹20209

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.

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