Relative sensor registration with two-step method for state estimation
State estimation suffers from some new challenging problems with a multi-platform multi-sensor observation system. An important problem for multisensor integration is that the data from the local sensors needs to be transformed into a common reference frame free of systematic bias or registration. I...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-05-01
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| Series: | Cognitive Computation and Systems |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0006 |
| _version_ | 1818352439393255424 |
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| author | Quanbo Ge Tianxiang Chen Tianxiang Chen Zhansheng Duan Mingxin Liu Zhuyun Niu |
| author_facet | Quanbo Ge Tianxiang Chen Tianxiang Chen Zhansheng Duan Mingxin Liu Zhuyun Niu |
| author_sort | Quanbo Ge |
| collection | DOAJ |
| description | State estimation suffers from some new challenging problems with a multi-platform multi-sensor observation system. An important problem for multisensor integration is that the data from the local sensors needs to be transformed into a common reference frame free of systematic bias or registration. In this study, the relative sensor registration problem is discussed. It aligns measurement from the global sensor with the local sensor under the assumptions that the global sensor is bias free and all biases reside with the local sensor. The traditional methods failed in the condition when attitude bias becomes large because the error caused by linearisation of rotation matrix increases with growing attitude bias. Motivated by this, a two-step method is established. By estimating the measurement bias through augmented extended Kalman filter in local sensor coordinate independent of attitude and location bias, and by introducing the unit quaternion method compute the attitude and location bias, the proposed method can avoid the problem the traditional methods faced. Simulation examples are provided to verify the proposed method by comparing with the existing linear least square algorithm. |
| first_indexed | 2024-12-13T18:53:39Z |
| format | Article |
| id | doaj.art-fd6a6cf973e44a8fb53efeb0bd945c97 |
| institution | Directory Open Access Journal |
| issn | 2517-7567 |
| language | English |
| last_indexed | 2024-12-13T18:53:39Z |
| publishDate | 2019-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cognitive Computation and Systems |
| spelling | doaj.art-fd6a6cf973e44a8fb53efeb0bd945c972022-12-21T23:34:53ZengWileyCognitive Computation and Systems2517-75672019-05-0110.1049/ccs.2018.0006CCS.2018.0006Relative sensor registration with two-step method for state estimationQuanbo Ge0Tianxiang Chen1Tianxiang Chen2Zhansheng Duan3Mingxin Liu4Zhuyun Niu5Shenzhen Institute of Guangdong Ocean UniversityInstitute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi UniversityInstitute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi UniversityCenter for Information Engineering Science Research, Xian Jiaotong UniversityCollege of Information Science and Engineering, Yanshan UniversityNorth Automatic Control Technology InstituteState estimation suffers from some new challenging problems with a multi-platform multi-sensor observation system. An important problem for multisensor integration is that the data from the local sensors needs to be transformed into a common reference frame free of systematic bias or registration. In this study, the relative sensor registration problem is discussed. It aligns measurement from the global sensor with the local sensor under the assumptions that the global sensor is bias free and all biases reside with the local sensor. The traditional methods failed in the condition when attitude bias becomes large because the error caused by linearisation of rotation matrix increases with growing attitude bias. Motivated by this, a two-step method is established. By estimating the measurement bias through augmented extended Kalman filter in local sensor coordinate independent of attitude and location bias, and by introducing the unit quaternion method compute the attitude and location bias, the proposed method can avoid the problem the traditional methods faced. Simulation examples are provided to verify the proposed method by comparing with the existing linear least square algorithm.https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0006nonlinear filtersstate estimationkalman filterssensor fusionmatrix algebralinearisation techniquesrelative sensor registration problemglobal sensorlocal sensorattitude biastwo-step methodlocation biasmultiplatform multisensor observation systemsystematic biasmeasurement bias estimationstate estimationrotation matrixunit quaternion methodattitude biaslinear least square algorithm |
| spellingShingle | Quanbo Ge Tianxiang Chen Tianxiang Chen Zhansheng Duan Mingxin Liu Zhuyun Niu Relative sensor registration with two-step method for state estimation Cognitive Computation and Systems nonlinear filters state estimation kalman filters sensor fusion matrix algebra linearisation techniques relative sensor registration problem global sensor local sensor attitude bias two-step method location bias multiplatform multisensor observation system systematic bias measurement bias estimation state estimation rotation matrix unit quaternion method attitude bias linear least square algorithm |
| title | Relative sensor registration with two-step method for state estimation |
| title_full | Relative sensor registration with two-step method for state estimation |
| title_fullStr | Relative sensor registration with two-step method for state estimation |
| title_full_unstemmed | Relative sensor registration with two-step method for state estimation |
| title_short | Relative sensor registration with two-step method for state estimation |
| title_sort | relative sensor registration with two step method for state estimation |
| topic | nonlinear filters state estimation kalman filters sensor fusion matrix algebra linearisation techniques relative sensor registration problem global sensor local sensor attitude bias two-step method location bias multiplatform multisensor observation system systematic bias measurement bias estimation state estimation rotation matrix unit quaternion method attitude bias linear least square algorithm |
| url | https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0006 |
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