An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer
The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3172501 |
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| author | Yuming Chen Wei Li Gaifang Xin Hai Yang Ting Xia |
| author_facet | Yuming Chen Wei Li Gaifang Xin Hai Yang Ting Xia |
| author_sort | Yuming Chen |
| collection | DOAJ |
| description | The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm. |
| format | Article |
| id | doaj-art-3058a757b3f94d45bbef4d32c44ea028 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3058a757b3f94d45bbef4d32c44ea0282025-02-03T01:00:57ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/31725013172501An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the ShearerYuming Chen0Wei Li1Gaifang Xin2Hai Yang3Ting Xia4School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaDepartment of Intelligent Equipment, Changzhou College of Information Technology, Changzhou 213164, ChinaSchool of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaThe strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.http://dx.doi.org/10.1155/2019/3172501 |
| spellingShingle | Yuming Chen Wei Li Gaifang Xin Hai Yang Ting Xia An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer Complexity |
| title | An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer |
| title_full | An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer |
| title_fullStr | An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer |
| title_full_unstemmed | An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer |
| title_short | An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer |
| title_sort | improved strong tracking kalman filter algorithm for the initial alignment of the shearer |
| url | http://dx.doi.org/10.1155/2019/3172501 |
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