Iterated extended Kalman filter for airborne electromagnetic data inversion

Karshakov, E. V.

doi:10.1080/08123985.2019.1593790

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Modelling in one dimension is not obsolete as is shown in Evgeny Karshakov's paper on modelling using extended Kalman filters. His results show that Kalman inversion modelling can converge rapidly to realistic results (Karshakov 2019).…”

Section: Introduction Aaron Davismentioning

confidence: 98%

Introduction

Davis

2020

Exploration Geophysics

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Section: Introduction Aaron Davismentioning

confidence: 98%

Introduction

Davis

2020

Exploration Geophysics

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“…The results showed that the denoised signal can significantly improve the prediction accuracy when used in earthquake precursor prediction [15]. Karshakov et al studied the essence of applying Kalman algorithm to solve the inverse problem in geophysical exploration, proposed a one-dimensional inversion method with lateral constraints using Kalman filter, and applied it to the processing of airborne measured data in the frequency domain under the condition of considering measurement noise, and achieved good results at the same time [16]. Therefore, using the tracking characteristics of Kalman algorithm, whether it is for high-frequency electromagnetic disturbance or low-frequency electromagnetic noise, can play a certain role in noise filtering and greatly improve the signal-to-noise ratio of the signal.…”

Section: Introductionmentioning

confidence: 99%

Design of Motion Attitude Measurement System and Correction Method in Semi‐Airborne Frequency Domain Electromagnetic Detection

Liu,

Su,

Zhang

et al. 2024

IEEJ Transactions Elec Engng

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In the semi‐airborne frequency domain electromagnetic detection, strong noise will be generated due to the attitude change of the receiving coil sensor. However, the existing attitude measurement system cannot be directly used in the semi‐airborne frequency domain electromagnetic system because of the requirement on the volume and accuracy of the instrument. In order to reduce the effect of attitude noise, this paper analyzes the cause and influence of attitude noise in the semi‐airborne frequency domain electromagnetic system, and presents the attitude noise correction method based on coefficient rotation matrix and Kalman algorithm. A high‐performance attitude sensor based on (Micro‐Electro‐Mechanical System (MEMS) technology is used to design an attitude measurement system for the semi‐airborne frequency domain electromagnetic system. The calculated result shows that the required accuracy of the roll angle and pitch angle of the attitude measurement system is less than 0.1°, whereas the required accuracy of the yaw angle is lower. In the field application, the apparent resistivity results show that the imaging profile corrected by attitude error is more consistent with local geological data than that without correction. Consequently, the attitude measurement system and correction method proposed in this paper can improve the detection accuracy of the semi‐airborne frequency domain electromagnetic system significantly. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…The aided navigation method based on the geophysical field provides a new idea and means to solve this problem [ 5 , 6 , 7 ]. The marine gravity field and geomagnetic field are two kinds of geophysical fields that we are mainly concerned about.…”

Section: Introductionmentioning

confidence: 99%

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

Gao

Chang

et al. 2021

Sensors

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The gravity gradient is the second derivative of gravity potential. A gravity gradiometer can measure the small change of gravity at two points, which contains more abundant navigation and positioning information than gravity. In order to solve the problem of passive autonomous, long-voyage, and high-precision navigation and positioning of submarines, an aided navigation method based on strapdown gravity gradiometer is proposed. The unscented Kalman filter framework is used to realize the fusion of inertial navigation and gravity gradient information. The performance of aided navigation is analyzed and evaluated from six aspects: long voyage, measurement update period, measurement noise, database noise, initial error, and inertial navigation system device level. When the parameters are set according to the benchmark parameters and after about 10 h of simulation, the results show that the attitude error, velocity error, and position error of the gravity gradiometer aided navigation system are less than 1 arcmin, 0.1 m/s, and 33 m, respectively.

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Iterated extended Kalman filter for airborne electromagnetic data inversion

Cited by 8 publications

References 7 publications

Introduction

Introduction

Design of Motion Attitude Measurement System and Correction Method in Semi‐Airborne Frequency Domain Electromagnetic Detection

An Aided Navigation Method Based on Strapdown Gravity Gradiometer

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