A Multisignal Wavelet Variance-Based Framework for Inertial Sensor Stochastic Error Modeling

2019 ◽  
Vol 68 (12) ◽  
pp. 4924-4936 ◽  
Author(s):  
Ahmed Radi ◽  
Gaetan Bakalli ◽  
Stephane Guerrier ◽  
Naser El-Sheimy ◽  
Abu B. Sesay ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Error Modeling ◽  
Stochastic Error ◽  
Wavelet Variance

Stochastic Error Modeling of MEMS Inertial Sensor with Implementation to GPS-Aided INU System for UAV Motion Sensing

2013 ◽  
Vol 464 ◽  
pp. 240-246 ◽  
Author(s):  
Chot Hun Lim ◽  
Tien Sze Lim ◽  
Voon Chet Koo
Keyword(s):  
Inertial Sensor ◽  
Micro Electro Mechanical System ◽  
Error Modeling ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Motion Sensing ◽  
Stochastic Error ◽  
Stochastic Errors ◽  
Global Positioning ◽  
Ar Modeling

The resided stochastic error in Micro-Electro-Mechanical-System (MEMS) Strapdown Inertial Navigation Unit (INU) had caused the instrument not being able to operate as a standalone device for navigation applications. The conventional Global Positioning System (GPS)-aided strapdown INU system is commonly adopted to tackle such issue. Note that the estimation accuracy of such system depends on how precise the modeling of the stochastic error. In this paper, a comprehensive stochastic error modeling through three distinct approaches, namely the Gauss-Markov (GM) modeling, the Allan Variance (AV) analysis, and the Autoregressive (AR) modeling, are presented. The analysis shows that AR model achieved better modeling accuracy than the other two approaches. Next, the modeled stochastic errors were implemented on a GPS-aided strapdown INU system for UAV airplane's motion sensing, and the results shown that AR model achieved lower RMSE than the GM model, indicating that AR model is more suitable than GM model in representing the stochastic error model of MEMS strapdown INU.

Stochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic Conditions

2017 ◽  
Vol 25 (4) ◽  
pp. 35-59
Author(s):  
A. Radi ◽  
◽  
S. Nassar ◽  
N. El-Sheimy ◽  
◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Error Modeling ◽  
Dynamic Conditions ◽  
Stochastic Error

Compensation for Stochastic Error of Gyros in a Dual-axis Rotational Inertial Navigation System

2015 ◽  
Vol 69 (1) ◽  
pp. 169-182 ◽  
Author(s):  
Zhichao Zheng ◽  
Songlai Han ◽  
Jin Yue ◽  
Linglong Yuan
Keyword(s):  
Navigation System ◽  
High Performance ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Tests ◽  
Stochastic Error ◽  
Stochastic Errors

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

Wavelet-Based Improvements for Inertial Sensor Error Modeling

2016 ◽  
Vol 65 (12) ◽  
pp. 2693-2700 ◽  
Author(s):  
Stephane Guerrier ◽  
Roberto Molinari ◽  
Yannick Stebler
Keyword(s):  
Inertial Sensor ◽  
Error Modeling ◽  
Sensor Error
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