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

doi:10.3969/j.issn.0372-2112.2014.01.026

电子学报

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. 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-liang^1,2, WEN Cheng-lin^2,3

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

摘要/Abstract

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

中图分类号:

TP391

徐立中, 冯肖亮, 文成林. 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[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2014.01.026.

XU Li-zhong, FENG Xiao-liang, WEN Cheng-lin. Sequential Fusion Filtering for Networked Multi-Sensor Systems Based on Noise Estimation[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2014.01.026.

参考文献

[1] 钱志鸿,王义君.物联网技术与应用研究[J].电子学报,2012,40(5):1023-1029. Qian Zhi-hong,Wang Yi-jun.Iot technology and application [J].Acta Electronica Sinica,2012,40(5):1023-1029.(in Chinese)
[2] 王慧斌,陈哲,王鑫,等.基于随机有限集的UPF-CPHD多目标跟踪[J].通信学报,2012,33(12):147-153. Wang Huibin,Chen Zhe,Wang Xin,et al.Random finite sets based UPF-CPHD multi-object tracking [J].Journal on Communications,2012,33(12):147-153.(in Chinese)
[3] 徐梦溪,徐枫,等.优化-最小求解的广义总变分图像复原[J].中国图象图形学报,2011,16(7):1317-1325. Xu Mengxi,Xu Feng,et al.Image restoration using majorization-minimizaiton algorithm based on generalized total variation[J].Journal of Image and Graphics,2011,16(7):1317-1325.(in Chinese)
[4] Zhang Min,Wu Chunming,Hang Yue,et al.Robust dynamical virtual network provisioning [J].Chinese Journal of Electronics,2013,22(1):151-154.
[5] Tan Guoping,Li Yueheng.An adaptive HEC scheme with variable packet size for wireless real-time reliable multicast [J].Chinese Journal of Electronics,2012,21(2):299-303.
[6] Wen Chenglin,Liu Rongli,Chen Tongwen.Linear unbiased state estimation with random one-step sensor delay [J].Circuits Systems Signal Processing,2007,26(4):573-590.
[7] E Yaz,A Ray.Linear unbiased state estimation under randomly varying bounded sensor delay [J].Application Applied Mathematics Letters,1998,11(4):27-32.
[8] Su Chunlien,Lu Channan.Interconnected network state estimation using randomly delayed measurements [J].IEEE Transaction on Power Systems,2001,16(4):870-878.
[9] Chen B,Yu L,Zhang W A.Robust Kalman filtering for uncertain state delay systems with random observation delays and missing measurements [J].IET Control Theory and Applications,2011,17(5):1945-1954.
[10] Gao Huijun,Chen Tongwen.H∞ estimation for uncertain systems with limited communication capacity [J].IEEE Transaction on Automatic Control,2007,52(11):2070-2084.
[11] Song Hongbo,Yu Li,Zhang Wenan.H∞ filtering of network-based systems with random delay [J].Signal Processing,2009,89(4):615-622.
[12] Shen Bo,Wang Zidong,Shu Huisheng,Wei Guoliang.H∞ filtering for nonlinear discrete-time stochastic systems with randomly varying sensor delays [J].Automatica,2009,45(4):1032-1037.
[13] 刘金良.一类网络环境下的离散线性系统的可靠性滤波器设计研究[J].电子学报,2012,40(12):2557-2561. Liu Jinliang.Networked-based reliable filter design for a class of discrete linear systems [J].Acta Electronica Sinica,2012,40(12):2557-2561.(in Chinese)
[14] Liang Yan,Chen Tongwen,Pan Quan.Optimal linear state estimator with multiple packet dropouts [J].IEEE Transaction on Automatic Control,2010,55(6):1428-1433.
[15] Sun Shuli,Xie Lihua,Xiao Wendong,Yeng Chai Soh.Optimal linear estimation for systems with multiple packet dropouts [J].Automatica,2008,44(5):1333-1342.
[16] Mehrdad Sahebsara,Chen Tongwen,Sirish L Shah.Optimal H∞ filtering in networked control systems with multiple packet dropouts [J].Systems & Control letters,2008,57:696-702.
[17] Cai Yunze,Xu Li,et al.Study on Robust H∞ filtering in networked environments [J].International Journal of Automation and Computing,2011,8(4):465-471.
[18] B Sinopoli,L Schenato,et al.Kalman filtering with intermittent observations .IEEE Transaction on Automatic Control,2004,49(9):1453-1463.
[19] Andrea Censi.Kalman filtering with intermittent observations:Convergence for semi-markov chains and an intrinsic performance measure [J].IEEE Transaction on Automatic Control,2011,56(2):376-381.
[20] 韩崇昭,朱洪艳,段战胜.多源信息融合(第二版) [M].北京:清华大学出版社,2010. Han Chongzhao,Zhu Hongyan,Duan Shengan.Multi-sensor Information Fusion (Second Edition) [M].Beijing:Tsinghua University Press,2010.(in Chinese)
[21] Bar-Shalom Yaakov,Mahendra Mallick,Huimin Chen,Robert Washburn.One-step solution for the general out-of-sequence-measurement problem in tracking [A].Proceedings of 2002 IEEE Aerospace Conference [C].Montana,USA:IEEE,2002.1551-1559.
[22] Bar-Shalom Yaakov,Huimin Chen,Mahendra Mallick.One-step solution for the multistep out-of-sequence -measurement problem in tracking [J].IEEE Transactions on Aerospace and Electronic Systems,2004,40(1):27-37.
[23] 余安喜,杨宏文,胡卫东,等.无序量测的次优递推滤波器[J].电子学报,2004,32(6):960-964. Yu Anxi,Yang Hongwen,HuWeidong,et al.A suboptimal recursive filter for out-of-sequence measurements [J].Acta Electronica Sinica,2004,32(6):960-964.(in Chinese)
[24] Feng Xiaoliang,Ge Quanbo,Wen Chenglin.Optimal update with one step out-of-sequence measurements for wireless multisensor network [A].Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition [C].Hong Kong:[s.n.],2008.826-831.
[25] Zhang Huanshui,Xie Lihua,et al.A reorganized innovation approach to linear estimation [J].IEEE Transaction on Automatic Control,2004,49(10):1810-1814.
[26] Wen Cheng-lin,Ge Quan-bo,Feng Xiao-liang.Hybrid filter with predict-estimator and compensator for the linear time invariant delayed system [J].Journal of Electronics (China),2009,26(5):666-672.
[27] Ge Quanbo,Xu Tingliang,Feng Xiaoliang,Wen Chenglin.Universal delayed Kalman filter with measurement weighted summation for the linear time invariant system .Chinese Journal of Electronics,2011,20(1):67-72.
[28] Mallick Mahendra,Zhang Keshu.Optimal multiple-lag out-of-sequence measurement algorithm based on generalized smoothing framework [A].Proceedings of SPIE-Signal and Data Processing of Small Targets [C].California,USA:SPIE,2005,5913:591308.1-591308.13.
[29] Zhang Keshu,Li X Rong,Zhu Yunmin.Optimal update with out-of-sequence measurements [J].IEEE Transactions on Signal Processing,2005,53(6):1992-2004.
[30] Shen X J,Zhu Y M,Song E B,et al.Optimal centralized update with multiple local out-of-sequence measurements [J].IEEE Transaction on Signal Processing,2009,57(4):1551-1562.
[31] Ge Quanbo,Wen Chenglin.Optimal algorithm for out-of -sequence estimates with arbitrary communication delays [A].Proceedings of the 3rd International Workshop on Signal Design and Its Applications in Communications [C].Chengdu,China:[s.n.],2007.323-327.

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

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

[1]	孟宪波, 鲍长春. 基于最小控制GARCH模型的噪声估计算法[J]. 电子学报, 2016, 44(3): 747-752.
[2]	邓峰, 鲍枫, 鲍长春. 基于MPEG-AAC编码器的压缩域音频增强方法[J]. 电子学报, 2014, 42(7): 1410-1418.
[3]	辛吉荣, 陆路希, 包昕, 程建. 基于时频稀疏性的跳频信号背景噪声估计算法[J]. 电子学报, 2014, 42(10): 1932-1937.
[4]	吴锡, 周激流, 谢明元. 改进LMMSE的弥散加权磁共振图像Rician噪声复原[J]. 电子学报, 2013, 41(4): 717-721.
[5]	颜鑫, 李应. 利用抗噪幂归一化倒谱系数的鸟类声音识别[J]. 电子学报, 2013, 41(2): 295-300.
[6]	王炫名;张晓林;景晓军. 基于改进型的三角模准则的融合滤波算法[J]. 电子学报, 2011, 39(6): 1327-1330.
[7]	葛泉波;冯肖亮;徐廷梁. 不确定无序量测系统的最优网络化状态估计[J]. 电子学报, 2011, 39(3A): 7-13.
[8]	程履帮. OFDMA系统中基于LMMSE信道估计算法的改进及其性能分析[J]. 电子学报, 2008, 36(9): 1782-1785.
[9]	屈代明. 线性预编码OFDM系统的迭代接收机[J]. 电子学报, 2004, 32(12): 1992-1996.