Document Type : Research Paper

Authors

• Fereydoon Nobakht Ersi ¹
• Abdolreza Safari ²
• Mohammad Ali Sharifi ³

¹ Ph.D student of geodesy, Faculty of Engineering, University of Tehran

² Associate professor of Surveying and Geospatial Engineering, Faculty of Engineering, University of Tehran

³ Associate professor of Surveying and Geospatial Engineering, Faculty of Engineering , University of Tehran

Abstract

The main purpose of the present paper is to use the ARMA probability models to model the time series of the daily positions of GPS permanent station.Daily Locations of the LLAS permanent station in the Southern California region have been selected from the SCIGN network, covering a period of seven years from January 2000 to December 2006, to establish a time series of position and to analyze it. Based on the time series of the daily position and using the weighted least squares, the geodetic parameters such as linear trend, annual and semi-annualfluctuations, as well as offsets,have been simultaneously estimated for the LLAS permanent station. In this study, Auto correlation Functions (ACF) and Partial Auto Correction functions (PACF) are used as the study tools for identifying the time series behavior of daily position of GPS permanent station and provide the possibility to examine the dependency of the position time series daily data. Given that several different probabilistic models may be appropriate for a daily position time series, therefore,the Akaike Information Criterion has been used at the stage of identifying and selecting the useful model. In this study, numerical results show that the best autoregressive moving average (ARMA) probabilistic model for the LLAS permanent station is ARMA (1, 1) for direction N. Also, the ARMA (2, 1) probabilistic model is the most appropriate model for direction E, while the ARMA (1, 2) probabilistic model is the best model for direction U. After estimating an appropriate probabilistic model for the time series of the daily position of the GPS permanent station, it is possible to predict the time series of the position along with the trend and seasonal components.

Keywords

• Time Series
• GPS
• Akaike Information Criterion
• ARMA
• Auto Correlation Function