| 引用本文: | 张新雨,任梦姣,弋英民,张子悦,武舒月.一种混合自适应重采样的智能粒子滤波[J].控制理论与应用,2026,43(2):346~354.[点击复制] |
| ZHANG Xin-yu,REN Meng-jiao,YI Ying-min,ZHANG Zi-yue,WU Shu-yue.An intelligent particle filter with hybrid adaptive resampling[J].Control Theory & Applications,2026,43(2):346~354.[点击复制] |
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| 一种混合自适应重采样的智能粒子滤波 |
| An intelligent particle filter with hybrid adaptive resampling |
| 摘要点击 137 全文点击 21 投稿时间：2023-09-05 修订日期：2024-10-16 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2024.30599 |
| 2026,43(2):346-354 |
| 中文关键词 信息论与信号处理 状态估计 粒子滤波 M-H重采样 高斯变异 自适应方差 |
| 英文关键词 information theory and signal processing state estimation particle filter M-H resampling Gaussian variation adaptive variance |
| 基金项目 国家自然科学基金项目(62003261, 62273273, 62073258), 陕西省科技创新团队项目(2023―CX―TD―01)资助. |
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| 中文摘要 |
| 粒子滤波对非线性非高斯系统具有较好的估计性能, 但引入重采样技术后, 粒子多样性匮乏一直是影响粒
子滤波估计精度的关键问题. 为此, 本文提出一种混合自适应重采样的智能粒子滤波方法, 该方法首先在混合自适
应Metropolis-Hastings(M-H)重采样基础上设计了高斯变异的自适应协方差矩阵计算函数; 其次, 提出了采用“优胜
劣汰”模式的接受拒绝准则函数; 最后, 对有效粒子集合进行实时更新, 改善了粒子集合的粒子质量并提高了粒子
滤波的精度. 利用两个一维非线性模型和一个高维非线性模型进行仿真, 以验证本文方法的有效性. 实验结果表明,
与现有重采样方法相比, 本文方法能够有效地改善重采样后的粒子质量, 提高粒子滤波的估计精度. |
| 英文摘要 |
| Particle filter (PF) has good estimation performance for nonlinear and non-Gaussian systems, but the lack
of particle diversity has always been the vital problem affecting the estimation accuracy of PF after the introduction of
resampling technology. Therefore, an intelligent PF method based on hybrid adaptive resampling is proposed. Firstly, a
function of computing the covariance matrix adaptively for Gaussian variation is designed in this method on the basis of
hybrid adaptive Metropolis-Hastings (M-H) resampling. Secondly, an acceptance and rejection criterion function using the
mode of survival of the fittest is developed. Finally, the effective particle set is updated in real time to improve the quality
of the particle set and the accuracy of the PF. Two one-dimensional nonlinear models and one high-dimensional nonlinear
model are used to verify the effectiveness of the proposed method. Experimental results show that the proposed method can
effectively improve the quality of the particle after resampling and improve the estimation accuracy of the PF compared
with the existing resampling methods. |
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