Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter

2022 ◽  
Vol 421 ◽  
pp. 126915
Author(s):  
A.S.M. Bakibillah ◽  
Yong Hwa Tan ◽  
Junn Yong Loo ◽  
Chee Pin Tan ◽  
M.A.S. Kamal ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Missing Data ◽  
Extended Kalman Filter ◽  
Robust Estimation ◽  
Traffic Density

Estimation of Critical Platform Integrity Parameters in the Absence of Direct Measurements in the Context of Integrated Marine Monitoring Systems

2013 ◽  
Author(s):  
Soma Maroju ◽  
Kevin Delaney ◽  
Christopher Leon ◽  
Igor Prislin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kalman Filter ◽  
Missing Data ◽  
Extended Kalman Filter ◽  
Working Condition ◽  
Operational Risk ◽  
Monitoring Systems ◽  
Marine Monitoring ◽  
Direct Measurements

Integrated Marine Monitoring Systems (IMMS) are designed to help operators to reduce operational risk by providing information about the environment and the platform responses in real time. In spite of efforts to keep monitoring systems in working condition by following planned maintenance and upgrades, some sensors may fail intermittently or may generate spurious data. Quite often, intervention to repair or to replace a faulty sensor is either difficult, or even not feasible. This paper discusses various methods to estimate critical platform integrity parameters with satisfactory confidence in the cases when direct measurements are temporarily unavailable or questionable. Methods such as Artificial Neural Network and Extended Kalman Filter have been employed and specifically tuned to particular challenges. Estimated results for the missing data, such as platform position or riser loads, are reliable as they have been validated against historically good data. The merit of the paper is to present the methods that can increase reliability of the IMMS, enhance safety, reduce operational risk and decrease cost in maintaining expensive offshore systems.

Robust estimation of arc length in a GMAW process by an adaptive extended Kalman filter

2016 ◽  
Vol 38 (11) ◽  
pp. 1334-1344 ◽  
Author(s):  
Mohammad Mousavi Anzehaei ◽  
Mohammad Haeri
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Robust Estimation ◽  
Arc Length ◽  
Adaptive Extended Kalman Filter

scholarly journals A Robust Extended Kalman Filter Applied to Ultrawideband Positioning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chuanyang Wang ◽  
Houzeng Han ◽  
Jian Wang ◽  
Hang Yu ◽  
Deng Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Robust Estimation ◽  
Hypothesis Test ◽  
Systematic Errors ◽  
Random Errors ◽  
Model Errors ◽  
Global Test ◽  
Practical Applications ◽  
The Individual

Ultrawideband (UWB) is well-suited for indoor positioning due to its high resolution and good penetration through objects. The observation model of UWB positioning is nonlinear. As one of nonlinear filter algorithms, extended Kalman filter (EKF) is widely used to estimate the position. In practical applications, the dynamic estimation is subject to the outliers caused by gross errors. However, the EKF cannot resist the effect of gross errors. The innovation will become abnormally large and the performance and the reliability of the filter algorithm are inevitably influenced. In this study, a robust EKF (REKF) method accompanied by hypothesis test and robust estimation is proposed. To judge the validity of model, the global test based on Mahalanobis distance is implemented to assess whether the test statistical term exceeds the threshold for outlier detection. To reduce and eliminate the effects of the individual outlier, the robust estimation using scheme III of the Institute of Geodesy and Geophysics of China (IGGIII) based on local test of the normalized residual is performed. Meanwhile, three kinds of stochastic models for outliers are expressed by modeling the contaminated distributions. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed REKF for resisting the outliers. Simulation experiment results are given to demonstrate that the outliers following all the three kinds of contaminated distributions can be detected. The proposed REKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the REKF are equivalent to the EKF method. The measured experiment results show that the outliers will be generated in the nonline-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimized residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed REKF is effective to resist the effects of outliers and improves the positioning accuracy compared with least-squares (LS) and EKF method. Moreover, the adaptive filter and ranging error model should be considered to compensate the state model errors and ranging systematic errors respectively. Then, the measurement outliers will be detected more correctly, and the robust estimation will be used effectively.

Author response for "Improved speed and load torque estimations with adaptive fading extended Kalman filter"

2020 ◽  
Author(s):  
Emrah Zerdali ◽  
Recep Yildiz ◽  
Remzi Inan ◽  
Ridvan Demir ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Author Response ◽  
Load Torque
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