| 非线性/非高斯序贯贝叶斯滤波 |
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| 引用本文： | 刘凤霞,宫先仪.非线性/非高斯序贯贝叶斯滤波[J].杭州电子科技大学学报,2011(4):9-12. |
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| 作者姓名： | 刘凤霞 宫先仪 |
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| 作者单位： | 浙江大学信息与电子工程系;杭州应用声学研究所; |
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| 基金项目： | 国家自然科学基金资助项目(60702022); 国家安全重大基础基金资助项目(613110020102) |
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| 摘 要： | 序贯Bayesian滤波为Bayesian滤波的递归实现,为在线估计系统状态提供了一个合理的框架.序贯贝叶斯滤波是基于状态-空间模型的.在线性高斯状态-空间模型下,最佳序贯贝叶斯滤波为大家熟知的卡尔曼滤波.在非线性/非高斯状态-空间模型下,最佳序贯贝叶斯滤波不存在通用的解析解,基于卡尔曼滤波的方法和质点滤波方法为比较常...
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| 关 键 词： | 序贯贝叶斯滤波 状态-空间模型 卡尔曼滤波 质点滤波 |
Nonlinear/Non-Gaussian Bayesian Sequential Filtering |
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| LIU Feng-xia,GONG Xian-yi.Nonlinear/Non-Gaussian Bayesian Sequential Filtering[J].Journal of Hangzhou Dianzi University,2011(4):9-12. |
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| Authors: | LIU Feng-xia GONG Xian-yi |
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| Affiliation: | LIU Feng-xia1,GONG Xian-yi2(1.Department of Information Science and Electronic Engineering,Zhejiang University,Hangzhou Zhejiang 310027,China,2.National Key Laboratory of Science and Technology on Sonar,Hangzhou Applied Acoustics Research Institute,Hangzhou Zhejiang 310012,China) |
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| Abstract: | Bayesian sequential filtering,which is based on state-space model,is recursive implementation of Bayesian filtering,and provides a suitable framework for estimating the state of system on-line.For linear-Gaussian problems,optimal Bayesian sequential filtering is well-known Kalman filter.For nonlinear or non-Gaussian problems there is no general analytical expression for optimal Bayesian sequential filtering,algorithms based on Kalman and particle filters are the most popular suboptimal Bayesian Sequential f... |
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| Keywords: | Bayesian sequential filtering state-space model Kalman filter particle filter |
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