| 一种新的非线性/非高斯滤波方法 |
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| 引用本文： | 郭春,罗鹏飞.一种新的非线性/非高斯滤波方法[J].国防科技大学学报,2002,24(2):23-26. |
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| 作者姓名： | 郭春 罗鹏飞 |
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| 作者单位： | 国防科技大学电子科学与工程学院,湖南,长沙,410073 |
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| 摘 要： | 自主滤波方法是一种递归式贝叶斯估计方法 ,该方法采用一组抽样值来近似目标状态的概率密度函数 ,可用于非线性系统模型和观测模型、非高斯观测噪声条件下的滤波。将该算法与扩展卡尔曼滤波方法进行了比较 ,仿真结果表明 ,该算法性能优于扩展卡尔曼滤波方法
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| 关 键 词： | 目标跟踪 贝叶斯估计 自主滤波 |
| 文章编号： | 1001-2486(2002)02-0023-04 |
| 收稿时间： | 2001/7/13 0:00:00 |
| 修稿时间： | 2001年7月13日 |
Study of a Novel Nonlinear/Non-Gaussion Filtering Algorithm |
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| GUO Chun and LUO Pengfei.Study of a Novel Nonlinear/Non-Gaussion Filtering Algorithm[J].Journal of National University of Defense Technology,2002,24(2):23-26. |
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| Authors: | GUO Chun and LUO Pengfei |
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| Affiliation: | College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China;College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China |
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| Abstract: | Bootstrap filtering algorithm is a recursive Bayesian estimation algorithm Since in this algorithm the probability density function of the state to be estimated is approximated by a series of samples, it can be applied to the circumstance of nonlinear system model and observation model ,even non Gaussion noise The bootstrap filter is compared with the Extended Kalman Filter(EKF),the simulation results have shown that the performance of the bootstrap filter is better than that of EKF |
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| Keywords: | target tracking Bayesian estimation Bootstrap filtering |
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