A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises
In this paper, a novel robust Student’s t-based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/7075037 |
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| _version_ | 1849434795352784896 |
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| author | Yongtao Shui Xiaogang Wang Wutao Qin Yu Wang Baojun Pang Naigang Cui |
| author_facet | Yongtao Shui Xiaogang Wang Wutao Qin Yu Wang Baojun Pang Naigang Cui |
| author_sort | Yongtao Shui |
| collection | DOAJ |
| description | In this paper, a novel robust Student’s t-based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s t distributions. To avoid the process uncertainty induced by the heavy-tailed process noise, the scale matrix of the predictive PDF is modeled as an inverse Wishart distribution and estimated dynamically. Then, the predictive PDF and the likelihood PDF are transformed into a hierarchical Gaussian form to obtain the approximate solution of posterior PDF. Based on the variational Bayesian approximation method, the posterior PDF is approximated iteratively by minimizing the Kullback-Leibler divergence function. Based on the posterior PDF of the auxiliary parameters, the predicted covariance and measurement noise covariance are modified. And then the information matrix and information state are updated by summing the local information contributions, which are computed based on the modified covariance. Finally, the state, scale matrix, and posterior densities are estimated after fixed point iterations. And the simulation results for a target tracking example demonstrate the superiority of the proposed filter. |
| format | Article |
| id | doaj-art-e3bd98b0891147d5bfc3541f287c6bfc |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-e3bd98b0891147d5bfc3541f287c6bfc2025-08-20T03:26:31ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/70750377075037A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed NoisesYongtao Shui0Xiaogang Wang1Wutao Qin2Yu Wang3Baojun Pang4Naigang Cui5School of astronautics, Harbin Institute of Technology, Harbin, ChinaSchool of astronautics, Harbin Institute of Technology, Harbin, ChinaNanjing Research Institute of Electronics Technology, Nanjing, ChinaSchool of astronautics, Harbin Institute of Technology, Harbin, ChinaSchool of astronautics, Harbin Institute of Technology, Harbin, ChinaSchool of astronautics, Harbin Institute of Technology, Harbin, ChinaIn this paper, a novel robust Student’s t-based cubature information filter is proposed for a nonlinear multisensor system with heavy-tailed process and measurement noises. At first, the predictive probability density function (PDF) and the likelihood PDF are approximated as two different Student’s t distributions. To avoid the process uncertainty induced by the heavy-tailed process noise, the scale matrix of the predictive PDF is modeled as an inverse Wishart distribution and estimated dynamically. Then, the predictive PDF and the likelihood PDF are transformed into a hierarchical Gaussian form to obtain the approximate solution of posterior PDF. Based on the variational Bayesian approximation method, the posterior PDF is approximated iteratively by minimizing the Kullback-Leibler divergence function. Based on the posterior PDF of the auxiliary parameters, the predicted covariance and measurement noise covariance are modified. And then the information matrix and information state are updated by summing the local information contributions, which are computed based on the modified covariance. Finally, the state, scale matrix, and posterior densities are estimated after fixed point iterations. And the simulation results for a target tracking example demonstrate the superiority of the proposed filter.http://dx.doi.org/10.1155/2020/7075037 |
| spellingShingle | Yongtao Shui Xiaogang Wang Wutao Qin Yu Wang Baojun Pang Naigang Cui A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises International Journal of Aerospace Engineering |
| title | A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises |
| title_full | A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises |
| title_fullStr | A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises |
| title_full_unstemmed | A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises |
| title_short | A Novel Robust Student’s t-Based Cubature Information Filter with Heavy-Tailed Noises |
| title_sort | novel robust student s t based cubature information filter with heavy tailed noises |
| url | http://dx.doi.org/10.1155/2020/7075037 |
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