Comparison of Kalman filter estimates of zenith atmospheric path delays using the Global Positioning System and very long baseline interferometry

Radio Science ◽  
1992 ◽  
Vol 27 (6) ◽  
pp. 999-1007 ◽  
Author(s):  
David M. Tralli ◽  
Stephen M. Lichten ◽  
Thomas A. Herring
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Very Long Baseline Interferometry ◽  
Positioning System ◽  
Long Baseline ◽  
Global Positioning ◽  
Atmospheric Path

scholarly journals Current Status and Future Directions of the IERS RS/PC Predictions of UT1

2009 ◽  
Vol 5 (H15) ◽  
pp. 218-218 ◽  
Author(s):  
W. Wooden ◽  
B. Luzum ◽  
N. Stamatakos
Keyword(s):  
Global Positioning System ◽  
Very Long Baseline Interferometry ◽  
Current Status ◽  
Laser Ranging ◽  
Positioning System ◽  
Reference Systems ◽  
Future Directions ◽  
Long Baseline ◽  
Global Positioning ◽  
E Mail

The International Earth Rotation and Reference Systems Service (IERS) Rapid Service/Prediction Center (RS/PC) produces daily and weekly EOP combination and prediction solutions. The daily solutions are produced after 1700 UTC while the weekly EOP solutions are produced Thursday after 1700 UTC. These solutions include data from Atmospheric Angular Momentum (AAM) analysis and forecasts, Global Positioning System (GPS) solutions, Satellite Laser Ranging (SLR) solutions, and Very Long Baseline Interferometry (VLBI) solutions. The solutions are sent to roughly 700 people by e-mail per week and are picked up in roughly 40000 ftp downloads per month.

scholarly journals The Use of Kalman Filter Techniques for Ship Track Estimation

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Dynamic Systems ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Positioning System ◽  
Global Positioning ◽  
Systems Identification ◽  
Simulation Results ◽  
Marine Vessels

Unscented Kalman Filter (UKF) is a technique used in non-linear applications and dynamic systems identification (e.g. tracking marine vessels and ships) that require state and parameter estimation. This paper studies Kalman Filter (KF) based techniques for tracking ships using Global Positioning System (GPS) data. The present work proposes to exploit information from GPS sensors in order to track a ship in real-time. The absence and presence problem of a ship is handled by a applying KF theory to analyze GPS coordinates and compare current marine vessel routes to previously recorded ones. To study tracking performance, the system was implemented in C++ and simulation results demonstrate the feasibility and high accuracy of the proposed tracking method

scholarly journals A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

2015 ◽  
Vol 15 (6) ◽  
pp. 294-303 ◽  
Author(s):  
Zhibin Miao ◽  
Hongtian Zhang ◽  
Jinzhu Zhang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Vehicle Stability ◽  
Positioning System ◽  
Robust Method ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Global Positioning

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

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