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网络化多传感器系统的序贯式融合滤波——基于噪声估计方法

徐立中1, 冯肖亮1,2, 文成林2,3   

  1. 1. 河海大学计算机与信息学院, 江苏南京 211100;
    2. 河南工业大学电气工程学院, 河南郑州 450001;
    3. 杭州电子科技大学自动化学院系统科学与控制工程研究所, 浙江杭州 310018
  • 收稿日期:2012-11-01 修回日期:2013-05-15 出版日期:2014-01-25
    • 通讯作者:
    • 文成林
    • 作者简介:
    • 徐立中 男,1958年生,博士、教授、博士生导师,河海大学信息与通信工程一级学科博士点学科主任.主要研究方向:多传感器系统与信息处理、遥感遥测技术、复杂系统建模与优化等. E-mail:lzhxu@hhu.edu.cn 冯肖亮 男,1984年生,河海大学计算机与信息学院博士研究生.主要研究方向:多源信息融合,滤波理论及水利量测. E-mail:ioptmyloving@163.com
    • 基金资助:
    • 国家自然科学基金 (No.60934009,No.51179047,No.61174112,No.61172133); 国家自然科学基金重大研究计划培育项目 (No.91016020)

Sequential Fusion Filtering for Networked Multi-Sensor Systems Based on Noise Estimation

XU Li-zhong1, FENG Xiao-liang1,2, WEN Cheng-lin2,3   

  1. 1. College of Computer and Information Engineering, Hohai University, Nanjing, Jiangsu 211100, China;
    2. College of Electrical Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China;
    3. Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • Received:2012-11-01 Revised:2013-05-15 Online:2014-01-25 Published:2014-01-25
    • Supported by:
    • National Natural Science Foundation of China (No.60934009, No.51179047, No.61174112, No.61172133); Cultivation Project under Major Research Plan of National Natural Science Foundation of China (No.91016020)

摘要: 在网络化多传感器系统中,由于各传感器采集到的量测信息在经网络向融合中心传递的过程中,常会出现各种随时间变化的延迟现象,而处理该类系统融合滤波问题的现有方法又大都难以实现滤波过程实时性与滤波精度最优性的共赢.为此,本文在线性最小均方误差意义下,利用不同时刻状态间的递推关系和噪声估计方法,提出了一种实时、递归、最优的序贯式融合滤波器.首先利用状态间的递推关系,将不同时刻得到的量测信息转化为当前状态的伪量测信息.其次,利用新提出的噪声估计方法求解伪量测方程中增益噪声的估计值和用于滤波器设计的增益矩阵.然后,基于转化后的伪量测信息和求取的滤波增益矩阵实现对系统状态的最优估计.以此方法依次处理该融合周期内到达融合中心的各量测信息,建立起一种实时、递归、最优的序贯式融合滤波器.最后,用计算机仿真来验证新方法的有效性.

关键词: 网络化多传感器系统, 融合滤波, 线性最小均方误差, 噪声估计

Abstract: In networked multi-sensor systems,the measurements sampled by sensors are transmitted to the fusion center through communication network with various time-varying delay phenomenons.The existing methods on the filtering problems of these systems either lost the real time property of the filtering process or lost the optimality of the filtering accuracy.In this paper,a real-time recursive optimal sequential fusion filter is proposed in the sense of linear minimum mean square error (LMMSE),based on the relationship of system states at different sampled instants and a novel noise estimation method.Firstly,based on the relationship of system states at different sampled instants,the measurement received by the fusion center at different time,is re-modeled as a pseudo measurement of the current state.Secondly,a noise estimation method is presented to estimate the gain noises in the pseudo measurement and solve the filtering gain matrix in the filtering process.Thirdly,the optimal estimate of the current state is obtained based on the re-modeled pseudo measurement and the solved filtering gain matrix.A real-time recursive optimal sequential fusion filter is obtained to deal with all the received measurement in the current fusion period according to the above proposed method.Finally,a simulation example is exploited to show the effectiveness of the proposed method.

Key words: networked multi-sensor system, fusion filtering, linear minimum mean square error, noise estimation

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