iterated extended kalman filter
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Author(s):  
Zhaoyang Jin ◽  
Junbo Zhao ◽  
Lei Ding ◽  
Saikat Chakrabarti ◽  
Elena Gryazina ◽  
...  

2021 ◽  
Vol 2070 (1) ◽  
pp. 012092
Author(s):  
Amit Kumar Gautam ◽  
Sudipta Majumdar

Abstract This paper presents the state estimation of diode circuit using iterated extended Kalman filter (IEKF). The root mean square error (RMSE) based performance evaluation gives the superiority of the IEKF based estimation over extended Kalman filtering (EKF) based method.


Geophysics ◽  
2020 ◽  
pp. 1-69
Author(s):  
Xingguo Huang ◽  
Kjersti Solberg Eikrem ◽  
Morten Jakobsen ◽  
Geir Naevdal

Uncertainty quantification in the context of seismic imaging is important for interpretinginverted subsurface models and updating reservoir models. The limited illumination, noisydata and poor initial model in the seismic full waveform inversion (FWI) lead to inversionuncertainties. This is particularly true for anisotropic elastic FWI, which suffers from extra parameter trade-off problems. In this work, we address the uncertainty quantificationof anisotropic elastic FWI problem in the framework of Bayesian inference. Specially, weestimate the uncertainties of the subsurface elastic parameters in the Bayesian anisotropicelastic FWI by combining the iterated extended Kalman filter with an explicit representation of the sensitivity matrix with Green’s functions. The sensitivity matrix is based onthe integral equation approach, which is also within the context of nonlinear inverse scattering theory. We give the results of numerical tests with examples for anisotropic elasticmedia. They show that the proposed Bayesian inversion method can provide reasonablereconstructed results for the elastic coefficients of the stiffness tensor and the framework issuitable for accessing the uncertainties.


Author(s):  
Johannes Bureick ◽  
Sören Vogel ◽  
Ingo Neumann ◽  
Jakob Unger ◽  
Hamza Alkhatib

Abstract In engineering geodesy, the technical progress leads to various kinds of multi-sensor systems (MSS) capturing the environment. Multi-sensor systems, especially those mounted on unmanned aerial vehicles, subsequently called unmanned aerial system (UAS), have emerged in the past decade. Georeferencing for MSS and UAS is an indispensable task to obtain further products of the data captured. Georeferencing comprises at least the determination of three translations and three rotations. The availability and accuracy of Global Navigation Satellite System (GNSS) receivers, inertial measurement units, or other sensors for georeferencing is not or not constantly given in urban scenarios. Therefore, we utilize UAS-based laser scanner measurements on building facades. The building latter are modeled as planes in a three-dimensional city model. We determine the trajectory of the UAS by combining the laser scanner measurements with the plane parameters. The resulting implicit measurement equations and nonlinear equality constraints are covered within an iterated extended Kalman filter (IEKF). We developed a software simulation for testing the IEKF using different scenarios to evaluate the functionality, performance, strengths, and remaining challenges of the IEKF implemented.


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