Major Development Under Gaussian Filtering Since Unscented Kalman Filter
文献类型：期刊论文
| 作者 | Abhinoy Kumar Singh |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2020 |
| 卷号 | 7期号:5页码:1308-1325 |
| 关键词 | Bayesian framework cubature rule-based filtering Gaussian filters Gaussian sum and square-root filtering nonlinear filtering quadrature rule-based filtering unscented transformation |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2020.1003303 |
| 英文摘要 | Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements. Such problems appear in several branches of science and technology, ranging from target tracking to biomedical monitoring. A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering. The early Gaussian filters used a derivative-based implementation, and suffered from several drawbacks, such as the smoothness requirements of system models and poor stability. A derivative-free numerical approximation-based Gaussian filter, named the unscented Kalman filter (UKF), was introduced in the nineties, which offered several advantages over the derivative-based Gaussian filters. Since the proposition of UKF, derivative-free Gaussian filtering has been a highly active research area. This paper reviews significant developments made under Gaussian filtering since the proposition of UKF. The review is particularly focused on three categories of developments: i) advancing the numerical approximation methods; ii) modifying the conventional Gaussian approach to further improve the filtering performance; and iii) constrained filtering to address the problem of discrete-time formulation of process dynamics. This review highlights the computational aspect of recent developments in all three categories. The performance of various filters are analyzed by simulating them with real-life target tracking problems. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43035]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Abhinoy Kumar Singh. Major Development Under Gaussian Filtering Since Unscented Kalman Filter[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(5):1308-1325. |
| APA | Abhinoy Kumar Singh.(2020).Major Development Under Gaussian Filtering Since Unscented Kalman Filter.IEEE/CAA Journal of Automatica Sinica,7(5),1308-1325. |
| MLA | Abhinoy Kumar Singh."Major Development Under Gaussian Filtering Since Unscented Kalman Filter".IEEE/CAA Journal of Automatica Sinica 7.5(2020):1308-1325. |
入库方式： OAI收割
来源：自动化研究所
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