| 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.[点击复制] |
| GUO Chun,LUO Pengfei.Study of a Novel Nonlinear/Non-Gaussion Filtering Algorithm[J].Journal of National University of Defense Technology,2002,24(2):23-26[点击复制] |
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| 一种新的非线性/非高斯滤波方法 |
| 郭春, 罗鹏飞 |
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(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。 |
| 关键词: 目标跟踪 贝叶斯估计 自主滤波 |
| DOI： |
| 投稿日期：2001-07-13 |
| 基金项目: |
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| Study of a Novel Nonlinear/Non-Gaussion Filtering Algorithm |
| GUO Chun, LUO Pengfei |
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(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 algorithmSince 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 |
| Keywords: target tracking Bayesian estimation Bootstrap filtering |
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