Atmospheric density determination using high-accuracy satellite GPS data

2018 ◽  
Vol 61 (2) ◽  
pp. 204-211
Author(s):  
TingLing Ren ◽  
Juan Miao ◽  
SiQing Liu
Keyword(s):  
High Accuracy ◽  
Gps Data ◽  
Atmospheric Density ◽  
Density Determination

scholarly journals PENGARUH AKTIVITAS MATAHARI TERHADAP KERAPATAN ATMOSFER ATAS BERDASARKAN DATA POSISI GPS SATELIT LAPAN-A2 (SOLAR ACTVITY INFLUENCE ON THE UPPER ATMOSPHERIC DENSITY DERIVED FROM GPS DATA ON LAPAN-A2 SATELLITE)

2018 ◽  
Vol 14 (2) ◽  
pp. 127
Author(s):  
Tiar Dani ◽  
Rhorom Priyatikanto ◽  
Slamet Supriadi ◽  
Abdul Rachman ◽  
Amrullah A. Qadir
Keyword(s):  
Space Debris ◽  
Line Element ◽  
Satellite Orbit ◽  
Density Variation ◽  
Gps Data ◽  
Atmospheric Density ◽  
Atmospheric Models ◽  
Cross Track ◽  
Lower Temporal Resolution

Studies on atmospheric density were very important to obtain a correction factor for the atmospheric density model. Thus, improvement of atmospheric models accuracy, i.e. CIRA, JASCHIA, NRLMSISE, became important in its application for re-entry prediction, satellite tracking and mitigation of the collisions probability between active satellites with space debris. GPS equipment installed in LAPAN-A2 indirectly measured the upper atmospheric density variation in-situ from the satellite orbit path. Notwithstanding the measurement had a lower temporal resolution than using accelerometer, but still gives better resolution than using Two-Line Element (TLE) data. This study had successfully determined upper atmospheric density variation with a 10 second resolution using LAPAN-A2 GPS data. The LAPAN-A2 GPS data validated using In-track Radial Cross-track (RIC) had ± 2 km error compared to the TLE data. It was also found that there was influence of solar activity on atmospheric density changes obtained from the LAPAN-A2 GPS data. AbstrakStudi kerapatan atmosfer atas sangat penting untuk memperoleh faktor koreksi dari suatu model kerapatan atmosfer. Peningkatan akurasi dari model atmosfer yang telah ada (CIRA, JASCHIA, NRLMSISE) sangat penting dalam penerapannya untuk prediksi re-entry, penjejakan satelit dan prakiraan kemungkinan terjadinya tabrakan antara satelit aktif dengan sampah antariksa. Peralatan GPS yang terpasang di satelit LAPAN-A2 secara tidak langsung dapat melakukan pengukuran in-situ perubahan kerapatan atmosfer atas dari orbit yang dilaluinya, meskipun tingkat resolusi temporalnya masih lebih rendah dibandingkan menggunakan instrumen akselerometer tetapi masih jauh lebih baik dibandingkan menggunakan data Two-Line Element (TLE). Studi ini telah berhasil memperoleh variasi kerapatan atmosfer atas dengan resolusi 10 detik menggunakan data posisi GPS LAPAN-A2. Selain itu, diperoleh pula tingkat kesalahan dalam koordinat satelit (Radial Intrack Crosstrack - RIC) data TLE terhadap data posisi GPS LAPAN-A2 sebesar ± 2 km. Selain itu terlihat pula pengaruh aktivitas matahari terhadap perubahan kerapatan atmosfer atas yang diperoleh dari data posisi GPS LAPAN-A2

Determination of Particle Densities and Line Profiles in Plasmas by Resonance Interferometry: A Feasibility Study using Computer Simulated Refractivity Data

1995 ◽  
Vol 50 (10) ◽  
pp. 902-914 ◽  
Author(s):  
C. Haas ◽  
G. Pretzier ◽  
H. Jäger
Keyword(s):  
Spectral Line ◽  
Feasibility Study ◽  
Particle Density ◽  
Detection Limits ◽  
High Accuracy ◽  
Line Profiles ◽  
Actual Problem ◽  
Density Determination

AbstractThe principles of resonance interferometry are described with regard to two applications: High accuracy particle density determination within plasmas and interferometrical determination of spectral line profiles. The usability of this technique is investigated numerically, and physical limits are given for the regions in which resonance interferometry may be employed successfully. The discussion and the results are helt general for making it possible to decide whether or not to apply this method for an actual problem. An example (an object being longitudinally homogeneous with respect to the direction of light: end-on observation) shows how to use the presented results for calculating the detection limits of the method for a given object geometry.

Adaptive Measurement Model of Navigation by Stellar Refraction based on Multiple Models Switching

2014 ◽  
Vol 67 (4) ◽  
pp. 673-685 ◽  
Author(s):  
Bo Yang ◽  
Fan Si ◽  
Fan Xu ◽  
Wenlan Zhou
Keyword(s):  
Measurement Method ◽  
Control Algorithm ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
High Accuracy ◽  
Mode Switching ◽  
Atmospheric Density ◽  
Simulation Based ◽  
Adaptive Measurement

In recent years, navigation by stellar refraction has received considerable attention, having advantages of high accuracy, simple construction, and low cost. Nevertheless, there are many limitations to the precision and application of this method using a traditional measurement model. This article studies the changing pattern of atmospheric density, the disturbed atmospheric density model and measurement model of stellar refraction ranging from 20 km to 50 km. Furthermore, a control algorithm of multiple mode switching and an adaptive measurement model are proposed. With this method, any refracted starlight from the scope of between 20 km and 50 km can be captured and the measurement model at the appropriate height can be automatically established. Due to this, the reliability and practicality of navigation have been raised considerably. Accuracy of navigation using the adaptive measurement method is observed to improve by about 14%, using computer simulation based on an Unscented Kalman Filter (UKF).

scholarly journals There and back again – a zebra's tale

2020 ◽  
Vol 223 (23) ◽  
pp. jeb232140
Author(s):  
Hattie Bartlam-Brooks ◽  
Simon Wilshin ◽  
Tatjana Hubel ◽  
Stephen Hailes ◽  
Emily Bennitt ◽  
...  
Keyword(s):  
Angular Distribution ◽  
Confidence Interval ◽  
High Accuracy ◽  
Gps Data ◽  
Multiple Routes ◽  
Plains Zebra ◽  
Better Than

ABSTRACTAnimals need to navigate between resources such as water, food and shelter, and how they achieve this is likely to vary with species. Here, using high-accuracy GPS data, we studied repeated journeys made by wild plains zebra (Equus quagga) through a naturally vegetated environment to explore whether they consistently follow the same route through the area or whether they use a range of routes to reach their goal. We used a model to distinguish and quantify these two possibilities and show that our observations are consistent with the use of multiple routes. Our model performs better than assuming a uniform angular distribution of trajectories. The typical separation of the routes was found to be small (1.96 m), while the scale at which neighbouring trajectories are informative to direction of travel was found to be large (with a confidence interval of 1.19–26.4 m). Our observations are consistent with the hypothesis that zebra are able to navigate without having to return to previously used routes, instead using numerous different routes of similar trajectories.

The Rotational Motion of a Satellite and Atmospheric Density Determination

1975 ◽  
pp. 233-279 ◽  
Author(s):  
V. V. Beletskii ◽  
V. M. Grigorevsky ◽  
S. Ya. Kolesnik
Keyword(s):  
Rotational Motion ◽  
Atmospheric Density ◽  
Density Determination

scholarly journals Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model

2020 ◽  
Vol 13 (1) ◽  
pp. 429-445
Author(s):  
Xiaoxu Chen ◽  
Xiangdong Xu ◽  
Chao Yang
Keyword(s):  
Computational Complexity ◽  
Mobile Phone ◽  
Travel Time ◽  
Mobile Devices ◽  
Mixed Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Gps Data ◽  
Inference Method ◽  
Acceptable Accuracy

Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.

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