scholarly journals Particle Filter with Novel Nonlinear Error Model for Miniature Gyroscope-Based Measurement While Drilling Navigation

Sensors ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 371 ◽  
Author(s):  
Tao Li ◽  
Gannan Yuan ◽  
Wang Li
Keyword(s):  
Particle Filter ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling

scholarly journals Vibration Error Correction for the FOGs-Based Measurement in a Drilling System Using an Extended Kalman Filter

2021 ◽  
Vol 11 (14) ◽  
pp. 6514
Author(s):  
Lu Wang ◽  
Yuanbiao Hu ◽  
Tao Wang ◽  
Baolin Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Original Data ◽  
Calibration Method ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling ◽  
Drilling System ◽  
Vibration Error

Fiber-optic gyroscopes (FOGs)-based Measurement While Drilling system (MWD) is a newly developed instrument to survey the borehole trajectory continuously and in real time. However, because of the strong vibration while drilling, the measurement accuracy of FOG-based MWD deteriorates. It is urgent to improve the measurement accuracy while drilling. Therefore, this paper proposes an innovative scheme for the vibration error of the FOG-based MWD. Firstly, the nonlinear error models for the FOGs and ACCs are established. Secondly, a 36-order Extended Kalman Filter (EKF) combined with a calibration method based on 24-position is designed to identify the coefficients in the error model. Moreover, in order to obtain a higher accurate error model, an iterative calibration method has been suggested to suppress calibration residuals. Finally, vibration experiments simulating the drilling vibration in the laboratory is implemented. Compared to the original data, compensated the linear error items, the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While compensating for the nonlinear error items of the FOG-based MWD, the error of 3D borehole trajectory can be reduced by a ratio from 44.13% to 97.22%. In conclusion, compensation of the nonlinear error of FOG-based MWD could improve the trajectory survey accuracy under vibration.

scholarly journals Improving Particle Filter Performance by Smoothing Observations

2018 ◽  
Vol 146 (8) ◽  
pp. 2433-2446 ◽  
Author(s):  
Gregor Robinson ◽  
Ian Grooms ◽  
William Kleiber
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Error Model ◽  
Small Scale ◽  
Observation Error ◽  
Filter Performance ◽  
Multigrid Solvers ◽  
Small Scales ◽  
Spatially Extended ◽  
The Individual

AbstractThis article shows that increasing the observation variance at small scales can reduce the ensemble size required to avoid collapse in particle filtering of spatially extended dynamics and improve the resulting uncertainty quantification at large scales. Particle filter weights depend on how well ensemble members agree with observations, and collapse occurs when a few ensemble members receive most of the weight. Collapse causes catastrophic variance underestimation. Increasing small-scale variance in the observation error model reduces the incidence of collapse by de-emphasizing small-scale differences between the ensemble members and the observations. Doing so smooths the posterior mean, though it does not smooth the individual ensemble members. Two options for implementing the proposed observation error model are described. Taking a discretized elliptic differential operator as an observation error covariance matrix provides the desired property of a spectrum that grows in the approach to small scales. This choice also introduces structure exploitable by scalable computation techniques, including multigrid solvers and multiresolution approximations to the corresponding integral operator. Alternatively the observations can be smoothed and then assimilated under the assumption of independent errors, which is equivalent to assuming large errors at small scales. The method is demonstrated on a linear stochastic partial differential equation, where it significantly reduces the occurrence of particle filter collapse while maintaining accuracy. It also improves continuous ranked probability scores by as much as 25%, indicating that the weighted ensemble more accurately represents the true distribution. The method is compatible with other techniques for improving the performance of particle filters.

Particle Filter and Approximation Error Model for State Estimation in Hyperthermia

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Bernard Lamien ◽  
Helcio Rangel Barreto Orlande ◽  
Guillermo Enrique Eliçabe
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Approximation Error ◽  
Error Model ◽  
Estimation Problem ◽  
Transient Temperature ◽  
Model Parameters ◽  
Hyperthermia Treatment ◽  
Measurement Models ◽  
Particle Filter Algorithm

This work deals with numerical simulation of a hyperthermia treatment of skin cancer as a state estimation problem, where uncertainties in the evolution and measurement models, as well as in the measured data, are accounted for. A reduced model is adopted, based on a coarse mesh for the solution of the partial differential equations that describe the physical problem, in order to expedite the solution of the state estimation problem with a particle filter algorithm within the Bayesian framework of statistics. The so-called approximation error model (AEM) is used in order to statistically compensate for model reduction effects. The Liu and West algorithm of the particle filter, together with the AEM, is shown to provide accurate estimates for the temperature and model parameters in a multilayered region containing a tumor loaded with nanoparticles. Simulated transient temperature measurements from one sensor are used in the analysis.

Measurement-While-Drilling MWD Error Model Validation – Does the Model Reflect Reality?

2021 ◽  
Author(s):  
David Gutierrez ◽  
Chad Hanak
Keyword(s):  
Real Time ◽  
Service Providers ◽  
Error Model ◽  
Error Sources ◽  
Error Models ◽  
Sensor Error ◽  
Magnetic Model ◽  
Measurement While Drilling ◽  
Standard Deviations ◽  
Directional Sensor

Abstract It has been well documented that magnetic models and Measurement-while-Drilling (MWD) directional sensors are not free from error. It is for this reason that directional surveys are accompanied by an error model that is used to generate an ellipse of uncertainty (EOU). The directional surveys represent the highest probable position of the wellbore and the EOU is meant to encompass all of the possible wellbore positions to a defined uncertainty level. The wellbore position along with the individual errors are typically presumed to follow a Normal (Gaussian) Distribution. In order for this assumption to be accurate, 68.3% of magnetic model and directional sensor error should fall within plus or minus one standard deviation (1σ), 95.5% within two standard deviations (2σ), and 99.7% within three standard deviations (3σ) of the limits defined in the error model. It is the purpose of this study to evaluate the validity of these assumptions. The Industry Steering Committee on Wellbore Survey Accuracy (ISCWSA) provides a set of MWD error models that are widely accepted as the industry standard for use in wellbore surveying. The error models are comprised of the known magnetic model and MWD directional sensor error sources and associated limits. It is the purpose of this paper to determine whether the limits defined in the ISCWSA MWD error models are representative of the magnitude of errors observed in practice. In addition to the ISCWSA defined error model terms, this research also includes an analysis of the sensor twist error term and the associated limits defined in the Fault Detection, Isolation, and Recovery (FDIR) error model. This study is comprised of 138 MWD runs that were selected based on the criteria that they were processed using FDIR with overlapping gyro surveying to ensure highly accurate and consistent estimated values. The error magnitudes and uncertainties estimated by FDIR were compiled and analyzed in comparison to the expected limits outlined in the error models. The results conclude that the limits defined in the ISCWSA error models are not always representative of what is observed in practice. For instance, in U.S. land the assumed magnitudes of several of the error sources are overly optimistic compared to the values observed in this study. This means that EOUs with which wells are planned may not be large enough in some scenarios which could cause the operator to assume unanticipated additional risk. The final portion of this analysis was undertaken to test the hypothesis that preventative measures such as additional non-magnetic spacing are generally being taken by operators and directional service providers to minimize additional injected error when survey corrections are not being implemented while drilling the well. This hypothesis was tested by dividing the 138 MWD runs into Historical (survey corrections were not utilized in real-time) and Real-Time (survey corrections were utilized in real-time) categories. The results indicate that there are no significant differences in the error estimates between the Historical and Real-Time categories. This result in combination with the determination that the majority of the error model error terms should be categorized as fat-tail distributed indicate that proper well spacing and economics calculated using separation factor alone are insufficient without the use of survey corrections in Real-Time.

scholarly journals Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoyan Chen ◽  
Qiuju Zhang ◽  
Yilin Sun
Keyword(s):  
Calibration Method ◽  
Industrial Applications ◽  
Error Model ◽  
Robot Calibration ◽  
Positioning Accuracy ◽  
Nonlinear Error ◽  
Nonlinear Compensation ◽  
The Stability ◽  
Stability And Accuracy ◽  
Linear Piecewise

This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).

The Quaternion based Attitude Error for the Nonlinear Error Model of the INS

2021 ◽  
pp. 1-1
Author(s):  
Tiangao Zhu ◽  
Yong Liu ◽  
Wenkui Li ◽  
Kailong Li
Keyword(s):  
Error Model ◽  
Nonlinear Error ◽  
Attitude Error

scholarly journals Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Baiqiang Zhang ◽  
Hairong Chu ◽  
Tingting Sun ◽  
Hongguang Jia ◽  
Lihong Guo ◽  
...  
Keyword(s):  
Low Cost ◽  
Error Model ◽  
Integrated System ◽  
Error Prediction ◽  
Simulation Test ◽  
Accuracy Improvement ◽  
Unscented Transform ◽  
Nonlinear Error ◽  
Error Covariance ◽  
Mems Imu

The performance of MEMS-SINS/GPS integrated system degrades evidently during GPS outage due to the poor error characteristics of low-cost IMU sensors. The normal EKF is unable to estimate SINS error accurately after GPS outage owing to the large nonlinear error caused by MEMS-IMU. Aiming to solve this problem, a hybrid KF-UKF algorithm for real-time SINS/GPS integration is presented in this paper. The linear and nonlinear SINS error models are discussed, respectively. When GPS works well, we fuse SINS and GPS with KF with linear SINS error model using normal EKF. In the case of GPS outage, we implement Unscented Transform to predict SINS error covariance with nonlinear SINS error model until GPS signal recovers. In the simulation test that we designed, an evident accuracy improvement in attitude and velocity could be noticed compared to the normal EKF method after the GPS signal recovered.

scholarly journals Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhang Bing ◽  
Wang Xiaodong ◽  
Lu Hao ◽  
Hao Zhaojun ◽  
Gu Changchao
Keyword(s):  
Nonlinear Filtering ◽  
Simulation Analysis ◽  
Large Angle ◽  
Error Model ◽  
Statistical Characteristics ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Nonlinear Error ◽  
Estimation Precision ◽  
The Stability

When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

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