A Design of MIMU/GPS Integrated Navigation System

Zhang, Xiaoyu; Song, Cong

doi:10.4028/www.scientific.net/amm.278-280.1719

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…As shown in Figure 3, the state vector of the proposed RNCF consists of two types of components of geomagnetic vector. One of these is the desired magnetic vector in the body frame of the BHA (termed as vector b k m ), whose state can be propagated by the Coriolis equation using the angular velocity of the BHA [18]. This propagation process is described by the following: ω .…”

Section: Dynamical System Model Of Geomagnetic Vectormentioning

confidence: 99%

“…The other component of the state vector is the external magnetic interference (termed vector δm b k ), which can be approximatively characterized by a first-order Gaussian Markov (GM) process [18], the model of the magnetic interference is as follows:…”

Section: Dynamical System Model Of Geomagnetic Vectormentioning

confidence: 99%

“…Ref. [18] established a cut-off frequency-based error model of geomagnetic vector, and the azimuth was estimated through the fusion of triaxial gyroscopes and magnetometers. These studies aim to introduce aided azimuth sources, free of magnetic interference, into the magnetometer-based MWD system in order to achieve a high-precision azimuth measurement.…”

Section: Introductionmentioning

confidence: 99%

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IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering

Yang,

Zeng,

Xiong

et al. 2024

Sensors

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A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment.

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Section: Dynamical System Model Of Geomagnetic Vectormentioning

confidence: 99%

Section: Dynamical System Model Of Geomagnetic Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering

Yang,

Zeng,

Xiong

et al. 2024

Sensors

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Strapdown inertial navigation systems readings correction based on navigational data of other sensors and systems with intelligent selection of the priority adjuster

2020

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Strapdown inertial navigation systems (SINS) are one of the main components of the navigation systems of the drones and aircraft (including autonomous ones), but their readings need to be instanly corrected due to the constant accumulation of errors. This paper comprises the review of existing approaches to using one or more sensors or systems to correct the navigation data of SINS algorithms (herein after – correctors) using integrated information processing. A common disadvantage of the analysed approaches is the lack of flexibility concerning the types and the number of SINS correctors used, as well as the growth of computational burden due to the use of the measurement vectors of all the correctors in the process of forming the state vector of the system. This article proposes the use of the original adaptive scheme based on the selection of the least noisy data, taking into account environmental conditions, for the integrated processing of the SINS and the correctos’ navigation parameters. The essence of the approach is that the state vector is estimated on the basis of the most reliable corrector. This allows reducing the correlation of errors in the correctors’ measurement of navigational parameters, since only the measurement vectors (or vector) with best navigational data signal/noise ratio (received from the corresponding correctors) are used in forming the state vector. Furthermore, the proposed navigational data fusion scheme has a modular structure and greater flexibility in comparison with the loosely coupled systems, and also implies the use of an arbitrary number of correction sensors and systems regardless of the physical nature of their measurements.

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A Design of MIMU/GPS Integrated Navigation System

Cited by 2 publications

References 4 publications

IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering

IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering

Strapdown inertial navigation systems readings correction based on navigational data of other sensors and systems with intelligent selection of the priority adjuster

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