| 引用本文: | 莫以为, 萧德云.进化粒子滤波算法及其应用[J].控制理论与应用,2005,22(2):269~272.[点击复制] |
| MO Yi-wei, XIAO De-yun.Evolutionary particle filter and its application[J].Control Theory and Technology,2005,22(2):269~272.[点击复制] |
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| 进化粒子滤波算法及其应用 |
| Evolutionary particle filter and its application |
| 摘要点击 2308 全文点击 3040 投稿时间：2003-09-03 修订日期：2004-05-31 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.2.019 |
| 2005,22(2):269-272 |
| 中文关键词 粒子滤波算法 样本贫化 进化规划 状态估计 |
| 英文关键词 particle filter samples impoverishment evolutionary programming state estimation |
| 基金项目 国家高技术研究发展计划(863计划)资助项目(2002AA412510;2002AA412420). |
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| 中文摘要 |
| 样本贫化现象是应用粒子滤波算法的一个主要障碍,对估计长时间维持不变量的影响尤为严重.通过分析产生该现象的原因,本文引入进化规划算子构成进化粒子滤波算法,增加样本集的多样性而缓解样本贫化现象的影响,改善其估计与跟踪能力,仿真结果显示所提出的算法是可行的. |
| 英文摘要 |
| Sample impoverishment phenomenon is a main handicap to particle filter application,especially in those cases to estimate the parameter that remains constant for a long time.Based on the analysis of the causes of sample impoverishment,the evolutionary particle filter is proposed,in which evolutionary programming is introduced.The improved approach relieves the effect caused by samples impoverishment through ameliorating the diversity of samples set.Simulation results demonstrate the feasibility of proposed evolutionary particle filter. |