| 基于粒子优化的多模型粒子滤波算法 |
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| 引用本文： | 刘先省,胡振涛,金勇,杨一平.基于粒子优化的多模型粒子滤波算法[J].电子学报,2010,38(2):301-306. |
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| 作者姓名： | 刘先省 胡振涛 金勇 杨一平 |
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| 作者单位： | (1. 河南大学智能技术与系统重点实验室, 开封 475001;2.西北工业大学控制与信息研究所, 西安 710072) |
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| 基金项目： | 国家自然科学基金(No.60972119);;河南省科技厅基础与前项目(No.092300410158);;河南省教育自然基金(No.2008A510001) |
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| 摘 要： | 针对模型信息引入粒子采样过程中导致用于逼近当前时刻真实状态与模型的粒子数减少问题，本文给出了一种基于粒子优化的多模型粒子滤波算法．在算法实现中，对每个粒子运行一个扩展卡尔曼滤波器，结合扩展卡尔曼滤波中预测更新机制实现最新量测信息的有效利用，进而提升单个采样粒子对于真实系统状态和模型逼近的有效性．理论分析和仿真结果表明：新算法在系统状态估计的精度以及模型辨识的准确性方面均明显地优于交互式多模型粒子滤波算法和多模型粒子滤波算法．
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| 关 键 词： | 多模型粒子滤波 交互式多模型 扩展卡尔曼滤波 模型辨识 |
| 收稿时间： | 2007-12-21 |
| 修稿时间： | 2009-6-16 |
A Novel Multiple Model Particle Filter Algorithm Based on Particle Optimization |
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| LIU Xian-xing,HU Zhen-tao,JIN Yong,YANG Yi-ping.A Novel Multiple Model Particle Filter Algorithm Based on Particle Optimization[J].Acta Electronica Sinica,2010,38(2):301-306. |
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| Authors: | LIU Xian-xing HU Zhen-tao JIN Yong YANG Yi-ping |
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| Affiliation: | 1.Laboratory of Intelligent Technology and System;Henan University;Kaifeng;Henan 475001;China;2.Institute of Control and Information;Northwestern Polytechnical University;Xi'an;Shaanxi 710072;China |
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| Abstract: | For the adverse effect caused by the number decline of particles which are applied to implement the state estimation and model recognition,when model information is introduced into particle sampling process,a novel multiple model particle filter algorithm based on particle optimization is proposed.In the new algorithm,every particle is combined with extended Kalman filter,and the prediction and update mechanism of extended Kalman filter is used to realize the reasonable utilization of the latest observation... |
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| Keywords: | multiple model particle filter interacting multiple model extended Kalman filter model identification |
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