An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise
In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target track...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/1082837 |
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| _version_ | 1832552196591321088 |
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| author | Hongjian Wang Cun Li |
| author_facet | Hongjian Wang Cun Li |
| author_sort | Hongjian Wang |
| collection | DOAJ |
| description | In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target tracking. The algorithm adopts Gaussian mixture density function to approximately estimate the observation noise. The observation models based on Mini RadaScan for target tracking on offing are introduced, and the observation noise is modelled as glint noise. The Gaussian components are predicted and updated using CKF. A cost function is designed by integral square difference to update the weight of Gaussian components on the time update stage. Based on comparison experiments of constant angular velocity model and maneuver model with different algorithms, the proposed algorithm has the advantages of fast tracking response and high estimation precision, and the computation time should satisfy real-time target tracking requirements. |
| format | Article |
| id | doaj-art-f7b8e6701edb41af8a4d83deeab3e663 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-f7b8e6701edb41af8a4d83deeab3e6632025-02-03T05:59:29ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/10828371082837An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation NoiseHongjian Wang0Cun Li1College of Automation, Harbin Engineering University, Harbin 150001, ChinaCollege of Automation, Harbin Engineering University, Harbin 150001, ChinaIn order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target tracking. The algorithm adopts Gaussian mixture density function to approximately estimate the observation noise. The observation models based on Mini RadaScan for target tracking on offing are introduced, and the observation noise is modelled as glint noise. The Gaussian components are predicted and updated using CKF. A cost function is designed by integral square difference to update the weight of Gaussian components on the time update stage. Based on comparison experiments of constant angular velocity model and maneuver model with different algorithms, the proposed algorithm has the advantages of fast tracking response and high estimation precision, and the computation time should satisfy real-time target tracking requirements.http://dx.doi.org/10.1155/2016/1082837 |
| spellingShingle | Hongjian Wang Cun Li An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise Discrete Dynamics in Nature and Society |
| title | An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise |
| title_full | An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise |
| title_fullStr | An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise |
| title_full_unstemmed | An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise |
| title_short | An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise |
| title_sort | improved gaussian mixture ckf algorithm under non gaussian observation noise |
| url | http://dx.doi.org/10.1155/2016/1082837 |
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