An algorithm to estimate parameters and states of a nonlinear maneuvering target.
This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1847711 |
| _version_ | 1797764949400879104 |
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| author | S.N. Hosseini M. Haeri H. Khaloozadeh |
| author_facet | S.N. Hosseini M. Haeri H. Khaloozadeh |
| author_sort | S.N. Hosseini |
| collection | DOAJ |
| description | This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and multimode filters are used for unknown parameters’ estimation, and particle filter is used to estimate the posterior density function. Parameter estimation and mode determination could be used in the resampling phase to weight the particles in accordance with the target mode. The main advantage of the adaptive parameter estimation approach is its ability to provide a quick estimation of the abruptly changing parameters from noninformative prior knowledge and to do this for multiple unknown parameters. Simulation results show that the proposed algorithm performs better than the other input estimation and tracking methods. |
| first_indexed | 2024-03-12T20:04:02Z |
| format | Article |
| id | doaj.art-aeb6106cb21046a6b2b70f1f05d74bf5 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T20:04:02Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-aeb6106cb21046a6b2b70f1f05d74bf52023-08-02T02:16:34ZengTaylor & Francis GroupCogent Engineering2331-19162020-01-017110.1080/23311916.2020.18477111847711An algorithm to estimate parameters and states of a nonlinear maneuvering target.S.N. Hosseini0M. Haeri1H. Khaloozadeh2Islamic Azad University, Science and Research BranchSharif University of TechnologyK.N. Toosi University of TechnologyThis paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and multimode filters are used for unknown parameters’ estimation, and particle filter is used to estimate the posterior density function. Parameter estimation and mode determination could be used in the resampling phase to weight the particles in accordance with the target mode. The main advantage of the adaptive parameter estimation approach is its ability to provide a quick estimation of the abruptly changing parameters from noninformative prior knowledge and to do this for multiple unknown parameters. Simulation results show that the proposed algorithm performs better than the other input estimation and tracking methods.http://dx.doi.org/10.1080/23311916.2020.1847711maneuvering target trackingparticle filterposterior distribution functionmultimode particle filter methodstate and input estimationliu and west filter |
| spellingShingle | S.N. Hosseini M. Haeri H. Khaloozadeh An algorithm to estimate parameters and states of a nonlinear maneuvering target. Cogent Engineering maneuvering target tracking particle filter posterior distribution function multimode particle filter method state and input estimation liu and west filter |
| title | An algorithm to estimate parameters and states of a nonlinear maneuvering target. |
| title_full | An algorithm to estimate parameters and states of a nonlinear maneuvering target. |
| title_fullStr | An algorithm to estimate parameters and states of a nonlinear maneuvering target. |
| title_full_unstemmed | An algorithm to estimate parameters and states of a nonlinear maneuvering target. |
| title_short | An algorithm to estimate parameters and states of a nonlinear maneuvering target. |
| title_sort | algorithm to estimate parameters and states of a nonlinear maneuvering target |
| topic | maneuvering target tracking particle filter posterior distribution function multimode particle filter method state and input estimation liu and west filter |
| url | http://dx.doi.org/10.1080/23311916.2020.1847711 |
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