Integration of Code-Based Precise Point Positioning and Reduced Inertial Sensor System

2016 ◽  
Author(s):  
Ibrahim E. Hassan ◽  
Tashfeen B. Karamat ◽  
Ahmed El-Rabbany ◽  
Aboelmagd Noureldin
Keyword(s):  
Precise Point Positioning ◽  
Inertial Sensor ◽  
Sensor System ◽  
Point Positioning ◽  
Reduced Inertial Sensor System

scholarly journals Odometer Velocity and Acceleration Estimation Based on Tracking Differentiator Filter for 3D-Reduced Inertial Sensor System

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4501 ◽  
Author(s):  
Qing Zhang ◽  
Lianwu Guan ◽  
Dexin Xu
Keyword(s):  
Inertial Sensor ◽  
Numerical Differentiation ◽  
Differential Method ◽  
Sensor System ◽  
Phase Lag ◽  
Differential Signal ◽  
Tracking Differentiator ◽  
Reduced Inertial Sensor System ◽  
Noise Amplification ◽  
Navigation Accuracy

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS.

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

FOS-based modelling of reduced inertial sensor system errors for 2D vehicular navigation

2010 ◽  
Vol 46 (4) ◽  
pp. 298 ◽  
Author(s):  
Z. Shen ◽  
J. Georgy ◽  
M.J. Korenberg ◽  
A. Noureldin
Keyword(s):  
Inertial Sensor ◽  
Sensor System ◽  
Reduced Inertial Sensor System ◽  
System Errors

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

A Reduced Inertial Sensor System Based on MEMS for Wellbore Continuous Surveying While Horizontal Drilling

2018 ◽  
Vol 18 (14) ◽  
pp. 5662-5673 ◽  
Author(s):  
Lu Wang ◽  
Aboelmagd Noureldin ◽  
Umar Iqbal ◽  
Abdalla M. Osman
Keyword(s):  
Inertial Sensor ◽  
Sensor System ◽  
Horizontal Drilling ◽  
Reduced Inertial Sensor System
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