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

HU Zhen-tao; ZHANG Jin; HU Yu-mei; JIN Yong

doi:10.3969/j.issn.0372-2112.2017.04.015

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

更新时间：2025-07-16

- Multi-Sensor Ensemble Kalman Filtering Algorithm Based on Metropolis-Hastings Sampling
- Acta Electronica Sinica Vol. 45, Issue 4, Pages: 868-873(2017)
- 作者机构：
  
  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
  3. 河南大学图像处理与模式识别研究所,河南,开封,475004
  4. 西北工业大学自动化学院,陕西,西安,710072
- 作者简介：
- 基金信息：
  
  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)
- DOI：10.3969/j.issn.0372-2112.2017.04.015
  CLC： TP391
- Published Online：25 April 2017，
  
  Published：2017
- 稿件说明：
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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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2017.04.015.

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.

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.

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