Coal Mining Machine Localization Method Based on Non-Gaussian Summation Parallel Kalman Filter Group
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| الحاوية / القاعدة: | Processes vol. 13, no. 3 (2025), p. 694 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Chenrong Xi |u Institute of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100080, China<email>17691587289@163.com</email> (Y.Y.); <email>s15175104974@163.com</email> (H.S.) | |
| 245 | 1 | |a Coal Mining Machine Localization Method Based on Non-Gaussian Summation Parallel Kalman Filter Group | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Coal mining machine positioning technology is the key to realizing unmanned and intelligent mining of the comprehensive mining zone. Based on the traditional Strapdown Inertial Navigation System combined with Kalman-filtering coal mining machine positioning technology, non-integrity constraints are introduced, and the error of the output of the above system is filtered by an optimized Kalman filtering method proposed in this paper: non-Gaussian summation and a parallel Kalman filter bank. This method decomposes the non-Gaussian system into a linear combination of multiple Gaussian systems through the parallel Kalman filter group, then fuses the states occupying different weight coefficients and designs a method of Gaussian-term number trimming to solve the problem of parameter explosion in the filtering process, and ultimately obtains the optimal estimation of the positioning information of the coal mining machine. Experiments show that, for the coal mining machine positioning issue in the complex noise interference environment of intelligent mines, the non-Gaussian summation and parallel Kalman filter group method in this paper, compared with the traditional particle filtering method, greatly reduces the three-dimensional attitude error, three-dimensional velocity error, three-dimensional position error in the nine dimensional parameters of the estimation error, and the average estimation error. The average estimation error is reduced by 49%, 52%, 50%, 53%, 51%, 48.8%, 50.1%, 54%, and 51.3%, respectively, which significantly improves the positioning accuracy of coal mining machines, and has stronger real-time performance, stability, and accuracy in the coal mining machine positioning system. | |
| 653 | |a Navigation systems | ||
| 653 | |a Strapdown inertial navigation | ||
| 653 | |a Coal mining | ||
| 653 | |a Accuracy | ||
| 653 | |a Random variables | ||
| 653 | |a Normal distribution | ||
| 653 | |a Noise | ||
| 653 | |a Localization | ||
| 653 | |a Filter banks | ||
| 653 | |a Kalman filters | ||
| 653 | |a Inertial navigation | ||
| 653 | |a Velocity errors | ||
| 653 | |a Localization method | ||
| 653 | |a Velocity | ||
| 653 | |a Digital twins | ||
| 653 | |a Positioning devices (machinery) | ||
| 653 | |a Sensors | ||
| 653 | |a Mining machinery | ||
| 653 | |a Algorithms | ||
| 653 | |a Coal | ||
| 653 | |a Real time | ||
| 653 | |a Attitudes | ||
| 653 | |a Parameters | ||
| 653 | |a Positioning | ||
| 653 | |a Position errors | ||
| 700 | 1 | |a Zhang, Fan |u Institute of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100080, China<email>17691587289@163.com</email> (Y.Y.); <email>s15175104974@163.com</email> (H.S.); College of Control Engineering, Xinjiang Institute of Engineering, Urumqi 830000, China | |
| 700 | 1 | |a Yang, Yu |u Institute of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100080, China<email>17691587289@163.com</email> (Y.Y.); <email>s15175104974@163.com</email> (H.S.) | |
| 700 | 1 | |a Song, Hui |u Institute of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100080, China<email>17691587289@163.com</email> (Y.Y.); <email>s15175104974@163.com</email> (H.S.) | |
| 773 | 0 | |t Processes |g vol. 13, no. 3 (2025), p. 694 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181723913/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181723913/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181723913/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |