Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/1/152 |
| _version_ | 1798034450821414912 |
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| author | Hairong Chu Tingting Sun Baiqiang Zhang Hongwei Zhang Yang Chen |
| author_facet | Hairong Chu Tingting Sun Baiqiang Zhang Hongwei Zhang Yang Chen |
| author_sort | Hairong Chu |
| collection | DOAJ |
| description | In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment. |
| first_indexed | 2024-04-11T20:43:29Z |
| format | Article |
| id | doaj.art-7a0444859ae74bc3b6d964812a0ab1f4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:43:29Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7a0444859ae74bc3b6d964812a0ab1f42022-12-22T04:04:06ZengMDPI AGSensors1424-82202017-01-0117115210.3390/s17010152s17010152Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman FilterHairong Chu0Tingting Sun1Baiqiang Zhang2Hongwei Zhang3Yang Chen4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaIn airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.http://www.mdpi.com/1424-8220/17/1/152MEMS IMUtransfer alignmentadaptive incremental Kalman filterinformation fusion |
| spellingShingle | Hairong Chu Tingting Sun Baiqiang Zhang Hongwei Zhang Yang Chen Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter Sensors MEMS IMU transfer alignment adaptive incremental Kalman filter information fusion |
| title | Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter |
| title_full | Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter |
| title_fullStr | Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter |
| title_full_unstemmed | Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter |
| title_short | Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter |
| title_sort | rapid transfer alignment of mems sins based on adaptive incremental kalman filter |
| topic | MEMS IMU transfer alignment adaptive incremental Kalman filter information fusion |
| url | http://www.mdpi.com/1424-8220/17/1/152 |
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