基于Metropolis-Hastings采样的多传感器集合卡尔曼滤波算法

doi:10.3969/j.issn.0372-2112.2017.04.015

电子学报 ›› 2017, Vol. 45 ›› Issue (4): 868-873.DOI: 10.3969/j.issn.0372-2112.2017.04.015

基于Metropolis-Hastings采样的多传感器集合卡尔曼滤波算法

胡振涛¹, 张谨¹, 胡玉梅², 金勇¹

1. 河南大学图像处理与模式识别研究所, 河南开封 475004;
2. 西北工业大学自动化学院, 陕西西安 710072

收稿日期:2015-12-08 修回日期:2016-05-13 出版日期:2017-04-25
- 作者简介:
- 胡振涛 男,1979年6月出生于河南永城市,现为河南大学计算机与信息工程学院副教授、硕士生导师.主要研究方向为复杂系统建模与估计、非线性滤波.E-mail:hzt@henu.edu.cn;张谨女,1992年1月出生于内蒙古通辽市,现为河南大学计算机与信息工程学院硕士研究生.主要研究方向为智能信息处理、非线性滤波.E-mail:zj_henu@163.com;胡玉梅 女,1990年10月出生于河南永城市,现为西北工业大学自动化学院博士研究生.主要研究方向为多源信息融合、状态估计.E-mail:hym_henu@163.com;金勇男,1972年6月出生于河南开封市,现为河南大学计算机与信息工程学院教授、硕士生导师.主要研究方向为波束形成、分布式计算.E-mail:jy@henu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61300214）; 河南省高校科技创新团队支持计划 (No.13IRTSTHN021）; 中国博士后科学基金 (No.2014M551999）; 河南省高校青年骨干教师资助计划 (No.2013GGJS-026）

Multi-Sensor Ensemble Kalman Filtering Algorithm Based on Metropolis-Hastings Sampling

HU Zhen-tao¹, ZHANG Jin¹, HU Yu-mei², JIN Yong¹

1. Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng, Henan 475004, China;
2. College of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China

Received:2015-12-08 Revised:2016-05-13 Online:2017-04-25 Published:2017-04-25
- Supported by:
- National Natural Science Foundation of China (No.61300214); Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province (No.13IRTSTHN021); China Postdoctoral Science Foundation (No.2014M551999); Funding Project for Young Backbone Teachers of Universities in Henan Province (No.2013GGJS-026)

摘要/Abstract

摘要：

集合卡尔曼滤波是近年来发展起来的一种处理非线性系统估计的有效解决方法.针对标准集合卡尔曼滤波实现过程中，量测噪声不确定导致自举量测采样出现一致性偏差问题，提出了一种基于Metropolis-Hastings采样的多传感器集合卡尔曼滤波算法.首先，结合多传感器量测系统的物理特性和集合卡尔曼滤波中自举量测生成机理，构建多传感器条件下自举量测集合.其次，通过对多传感器自举量测似然度求解以及在量测接受概率函数合理设计基础上，利用Metropolis-Hastings采样策略实现有效量测的确认.新算法通过对多传感器量测中冗余和互补信息的提取与利用实现对一致性偏差的修正，进一步改善被估计系统状态的滤波精度.理论分析和仿真实验结果验证了算法的可行性和有效性.

关键词: 非线性滤波, 集合卡尔曼滤波, 自举量测, Metropolis-Hastings采样

Abstract:

Recently,ensemble Kalman filter is considered as an effective solution for the state estimation of nonlinear system.Aiming at the consistency deviation occurred in virtual measurement sampling process on account of measurement noise uncertainty,a novel multi-sensor ensemble Kalman filtering algorithm based on Metropolis-Hastings sampling is proposed.Firstly,combined with the physical properties of multi-sensor measurement system and the generation mechanism of bootstrapping measurement in ensemble Kalman filter,multi-sensor bootstrapping measurement set is structured.Secondly,through solving the likelihood of multi-sensor bootstrapping measurement and designing the probability function of measurement acceptance,validation measurement from multi-sensor bootstrapping measurement set is confirmed by Metropolis-Hastings sampling strategy.The new method corrects the consistency deviation appearing at bootstrapping measurement by means of the extraction and utilization for the redundancy and complementary information in multi-sensor measurement,and improves the filtering precision for the estimated system state.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of our proposed algorithm.

Key words: nonlinear filter, ensemble Kalman filter, bootstrapping measurement, Metropolis-Hastings sampling

中图分类号:

TP391

胡振涛, 张谨, 胡玉梅, 等. 基于Metropolis-Hastings采样的多传感器集合卡尔曼滤波算法[J]. 电子学报, 2017, 45(4): 868-873.

HU Zhen-tao, ZHANG Jin, HU Yu-mei, et al. Multi-Sensor Ensemble Kalman Filtering Algorithm Based on Metropolis-Hastings Sampling[J]. Acta Electronica Sinica, 2017, 45(4): 868-873.

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基于Metropolis-Hastings采样的多传感器集合卡尔曼滤波算法

Multi-Sensor Ensemble Kalman Filtering Algorithm Based on Metropolis-Hastings Sampling

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