A Full‐Spectrum Bedrock Thermal Expansion Model and Its Impact on the Global Positioning System Height Time Series

2020 ◽  
Vol 47 (1) ◽  
Author(s):  
Jintao Lei ◽  
Wu Chen ◽  
Zhao Li ◽  
Fei Li ◽  
Shengkai Zhang
2015 ◽  
Vol 16 (1) ◽  
pp. 71
Author(s):  
Eniuce Menezes de Souza ◽  
Daniele Barroca Marra Alves ◽  
Fernanda Lang Schumacher

The identification of the cyclical and seasonal variations can be veryimportant in time series. In this paper, the aim is to identify the presence ofcyclical or seasonal variations in the indices of the multipath effect on continuousGPS (Global Positioning System) stations. Due to the model used to obtain theseindices, there should not have cyclical variations in these series, at least due to themultipath effect. In order to identify the presence of cyclical variations in theseseries, correlograms and Fourier periodograms were analyzed. The Fisher test forseasonality was applied to confirm the presence of statistical significant seasonality.In addition, harmonic models were adjusted to check in which months of the yearthe cyclical effects are occurring in the multipath indices. The possible causes ofthese effects are pointed out, which will direct the upcoming investigations, as wellas the analysis and correlations of other series. The importance of this analysisis mainly due to the fact that errors in the collected signals of these stations willdirectly influence the accuracy of the results of the whole community that directlyor indirectly uses GPS data.


2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988525
Author(s):  
Di Zhao ◽  
Huaming Qian ◽  
Dingjie Xu

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.


2012 ◽  
Vol 4 (2) ◽  
pp. 1025-1067
Author(s):  
S. Rudenko ◽  
N. Schön ◽  
M. Uhlemann ◽  
G. Gendt

Abstract. Precise weekly positions of 403 Global Positioning System (GPS) stations located worldwide are obtained by reprocessing GPS data of these stations at the time span from 4 January 1998 until 29 December 2007. The used processing algorithm and models as well as the solution and results obtained are presented. Vertical velocities of GPS stations having tracking history longer than 2.5 yr are computed and compared with the estimates from the colocated tide gauges and other GPS solutions. Examples of typical behavior of station height changes are given and interpreted. The derived time series and vertical motions of continuous GPS at tide gauges stations can be used for correcting tide gauge estimates of regional and global sea level changes.


INTI TALAFA ◽  
2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Yaman Khaeruzzaman

Seiring dengan pesatnya kemajuan teknologi saat ini, kebutuhan manusia menjadi lebih beragam, termasuk kebutuhan akan informasi. Tidak hanya media informasinya yang semakin beragam, jenis informasi yang dibutuhkan juga semakin beragam, salah satunya adalah kebutuhan informasi akan posisi kita terhadap lingkungan sekitar. Untuk memenuhi kebutuhan itu sebuah sistem pemosisi diciptakan. Sistem pemosisi yang banyak digunakan saat ini cenderung berfokus pada lingkup ruang yang besar (global) padahal, dalam lingkup ruang yang lebih kecil (lokal) sebuah sistem pemosisi juga diperlukan, seperti di ruang-ruang terbuka umum (taman atau kebun), ataupun dalam sebuah bangunan. Sistem pemosisi lokal yang ada saat ini sering kali membutuhkan infrastruktur yang mahal dalam pembangunannya. Aplikasi Pemosisi Lokal Berbasis Android dengan Menggunakan GPS ini adalah sebuah aplikasi yang dibangun untuk memenuhi kebutuhan pengguna akan informasi lokasi dan posisi mereka terhadap lingkungan di sekitarnya dalam lingkup ruang yang lebih kecil (lokal) dengan memanfaatkan perangkat GPS (Global Positioning System) yang telah tertanam dalam perangkat smartphone Android agar infrastruktur yang dibutuhkan lebih efisien. Dalam implementasinya, Aplikasi Pemosisi Lokal ini bertindak sebagai klien dengan dukungan sebuah Database Server yang berfungsi sebagai media penyimpanan data serta sumber referensi informasi yang dapat diakses melalui jaringan internet sehingga tercipta sebuah sistem yang terintegrasi secara global. Kata kunci: aplikasi, informasi, pemosisi, GPS.


Author(s):  
Violet Bassey Eneyo

This paper examines the distribution of hospitality services in Uyo Urban, Nigeria. GIS method was the primary tool used for data collection. A global positioning system (GPS) Garmin 60 model was used in tracking the location of 102 hospitality services in the study area. One hypothesis was stated and tested using the nearest neighbour analysis. The finding shows evidence of clustering of the various hospitality services. The tested hypothesis further indicated that hospitality services clustered in areas that guarantee a sustainable level of patronage to maximize profit. Thus, the hospitality services clustered in selected streets in the metropolis while limited numbers were found outside the city’s central area.


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