Research on the Compensation Strategy of the Initial Alignment of the SINS Based on the Dynamic Model of the Shearer

Chen, Yuming; Li, Wei; Yang, Hai; Xia, Ting

doi:10.1109/access.2019.2904309

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…MEMS-INS/GNSS integration based on a nonlinear filter is a nonlinear time-varying system. We can summarize the steps of observable degree analysis of nonlinear time-varying systems based on [25].…”

Section: B Problems Analysis Of Traditional Methods and Common Methodsmentioning

confidence: 99%

“…Vehicle dynamics constraints mean that when a vehicle is traveling on the road, if sideslip and jump do not occur, its speeds in the direction of x-axis and z-axis are zero [25]. Using the method of vehicle dynamics constraints to assist inertial navigation system, it can complete autonomous correction and improve navigation accuracy without adding external auxiliary equipment [26].…”

Section: A Inspiration Of Vehicle Dynamics Constraintmentioning

confidence: 99%

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A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

et al. 2019

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In the traditional MEMS-INS/GNSS integration, velocity and position are regarded as measurement information. However, it will lead to unobservable of the heading angle, and the heading angle error will be divergent. Eventually, the navigation accuracy will be reduced either. In this paper, a novel approach of heading angle estimation based on Zero-Heading angle-Variation-Constraint (ZHVC) and Sequential-Adaptive Unscented Kalman Filter (SAUKF) algorithms is proposed for avoiding the heading angle unobservability and unstable of the filtering. First, we inspired by the vehicle dynamics and add new information to the original measurement vectors. This new measurement information is the difference of heading angle variations from the MEMS-INS and the theoretical value. Second, we separate the measurement update of Unscented Kalman Filter (UKF) to two parts by sequential method. One is the measurement information of velocity and position, the other one is the heading angle variation. Meanwhile, adaptive UKF only estimate the heading angle variation measurement noise covariance matrix in real-time. The simulation and experiment show that ZHVC can improve the observable degree and accuracy of heading angle than the common method. The SAUKF can estimate heading angle variation measurement noise covariance matrix in real-time. And the filter results are more stable in different motions of the vehicle.INDEX TERMS ZHVC, SAUKF, observable degree, measurement noise covariance matrix.

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Section: B Problems Analysis Of Traditional Methods and Common Methodsmentioning

confidence: 99%

Section: A Inspiration Of Vehicle Dynamics Constraintmentioning

confidence: 99%

A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

et al. 2019

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“…Yang Hai et al [ 9 ] used a motion constraint-assisted SINS zero velocity update model to improve the positioning accuracy during the operation process. Chen Yuming et al [ 10 ] proposed an initial alignment compensation strategy based on the mechanical model of coal mining machinery to solve the performance degradation problem of the underground initial alignment algorithm in coal mines. WU Gang et al [ 11 , 12 ] proposed an SNIS error compensation model through theoretical analysis and simulation experiments and conducted experimental verification.…”

Section: Introductionmentioning

confidence: 99%

Research on the Three-Machines Perception System and Information Fusion Technology for Intelligent Work Faces

Feng,

Fang,

Chen

et al. 2023

Sensors

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The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment’s state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines.

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“…Where conventional GPS [ 9 ] cannot be used in underground mine environments [ 10 ], UWB [ 11 , 12 ] positioning systems can be used in environments where GPS is denied. SINS has been successfully applied to the positioning of continuous coal mining machines [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines

Wang

Zhang

et al. 2023

Micromachines

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The application of an ultra-wideband (UWB) positioning system in a Global Positioning System (GPS) denial environment such as an underground coal mine, mainly focuses on position information and rarely involves information such as direction attitude. Position accuracy is often affected by multipath, non-visible ranges, base station layout, and more. We proposed an IMU-assisted UWB-based positioning system for the provision of positioning and orientation services to coal miners in underground mines. The Error-State Kalman Filter (ESKF) is used to filter the errors in the measured data from the IMU-assisted UWB positioning system to obtain the best estimate of the error for the current situation and correct for inaccuracies due to approximations. The base station layout of the IMU-assisted UWB positioning system was also simulated. The reasonable setting of the reference base station location can suppress multi-access interference and improve positioning accuracy to a certain extent. Numerous simulation experiments have been conducted in GPS denial environments, such as underground coal mines. The experimental results show the effectiveness of the method for determining the position, direction, and attitude of the coal miner under the mine, which provides a better reference value for positioning and orientation in a GPS rejection environment such as under the mine.

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Research on the Compensation Strategy of the Initial Alignment of the SINS Based on the Dynamic Model of the Shearer

Cited by 14 publications

References 29 publications

A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

A Heading Angle Estimation Approach for MEMS-INS/GNSS Integration Based on ZHVC and SAUKF

Research on the Three-Machines Perception System and Information Fusion Technology for Intelligent Work Faces

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines

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