[1]杨 春,郭 健,张 磊,等.采用无迹信息滤波的多传感器容错融合算法[J].南京理工大学学报(自然科学版),2017,41(03):269.[doi:10.14177/j.cnki.32-1397n.2017.41.03.001]
 Yang Chun,Guo Jian,Zhang Lei,et al.Multi-sensor fault-tolerant fusion algorithm usingunscented information filter[J].Journal of Nanjing University of Science and Technology,2017,41(03):269.[doi:10.14177/j.cnki.32-1397n.2017.41.03.001]
点击复制

采用无迹信息滤波的多传感器容错融合算法()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年03期
页码:
269
栏目:
出版日期:
2017-06-30

文章信息/Info

Title:
Multi-sensor fault-tolerant fusion algorithm usingunscented information filter
文章编号:
1005-9830(2017)03-0269-09
作者:
杨 春郭 健张 磊陈庆伟
南京理工大学 自动化学院,江苏 南京 210094
Author(s):
Yang ChunGuo JianZhang LeiChen Qingwei
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
无迹信息滤波 多传感器 容错 数据融合 模糊逻辑 卡方检验
Keywords:
unscented information filter multi-sensor fault tolerant data fusion fuzzy logic Chi-square test
分类号:
TP273
DOI:
10.14177/j.cnki.32-1397n.2017.41.03.001
摘要:
针对故障检测阈值难以确定和系统非线性较强时精度差的问题,提出了1种新的多传感器容错融合算法。各量测子系统采用无迹信息滤波,不需要对系统进行线性化,因而能避免线性化误差。设计基于模糊逻辑的故障检测算法。利用卡方检验值计算子系统有效概率。根据子系统的有效概率自适应隔离故障子系统,处理故障检测阈值难以选取的问题。将该文算法应用于捷联惯性导航系统/北斗2/全球定位系统(SINS/BD2/GPS)组合导航系统中,仿真结果验证了其有效性。
Abstract:
A new multi-sensor fault-tolerant fusion algorithm is proposed for the poor accuracy of nonlinear data fusion systems and fault detection threshold selection problem.Unscented information filters are embedded into measurement subsystems,and the linearization error is avoided because the system need not be linearized.A fault detection algorithm based on fuzzy logic is designed.The subsystem validation probability is calculated using Chi-square test.Fault subsystems are isolated adaptively according to the validation probability of the subsystem,and the difficulty of selecting fault detection threshold is solved.The proposed algorithm is applied to a strap-down inertial navigation system/beidou2/global position system(SINS/BD2/GPS)integrated navigation system,and the simulation results show the availability.

参考文献/References:

[1] 刘鸿飞,杨建栋,方丽英,等.加权数据融合算法在液化气监测中的应用[J].南京理工大学学报,2016,40(2):250-254.
Liu Hongfei,Yang Jiandong,Fang Liying,et al.Application of weighted data fusion in liquefied gas monitoring[J].Journal of Nanjing University of Science and Technology,2016,40(2):250-254.
[2]高德民,钱焕延,严筱永,等.无线传感器网络最大生命期数据融合算法[J].南京理工大学学报,2012,36(1):55-60.
Gao Demin,Qian Huanyan,Yan Xiaoyong,et al.Maximum lifetime data aggregation algorithm for wireless sensor networks[J].Journal of Nanjing University of Science and Technology,2012,36(1):55-60.
[3]Bevilacqua M,Tsourdos A,Starr A,et al.Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems[C]//6th International Conference on Intelligent Systems,Modelling and Simulation.New York,USA:IEEE,2015:76-81.
[4]Fan Qigao,Wu Yaheng,Hui Jing,et al.Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking[J].The Scientific World Journal,2014:215303.
[5]Gao Wei,Zhang Ya,Wang Jianguo.A strapdown interial navigation system/Beidou/Doppler velocity log integrated navigation algorithm based on a Cubature Kalman filter[J].Sensors(Basel),2014,14(1):1511-1527.
[6]Khoshelham K,Zlatanova S.Sensors for indoor mapping and navigation[J].Sensors(Basel),2016,16(5):655.
[7]Liu Jin,Fang Jiancheng,Yang Zhaohua,et al.X-ray pulsar/Doppler difference integrated navigation for deep space exploration with unstable solar spectrum[J].Aerospace Science & Technology,2015,41:144-150.
[8]Li Zengke,Wang Jian,Li Binghao,et al.GPS/INS/odometer integrated system using fuzzy neural network for land vehicle navigation applications[J].Journal of Navigation,2014,67(6):1-17.
[9]Lu Kelin,Zhou Rui.Multi-sensor fusion for robust target tracking in the simultaneous presence of set-membership and stochastic Gaussian uncertainties[J].IET Radar,Sonar & Navigation,2016,11(4):621-628.
[10]Wang Lijuan,Wu Lifeng,Guan Yong,et al.Online sensor fault detection based on an improved strong tracking filter[J].Sensors(Basel),2015,15(2):4578-4591.
[11]Yang Hongtao,Gao Huibin,Liu Xin.Strong tracking filtering algorithm of randomly delayed measurements for nonlinear systems[J].Mathematical Problems in Engineering,2015(5):1-14.
[12]Zheng Wei,Wang Jing,Wang Zengfu.Multi-sensor fusion based real-time hovering for a quadrotor without GPS in assigned position[C]//The 28th Chinese Control and Decision Conference(2016 CCDC).Yinchuan,China:IEEE,2016:3605-3610.
[13]Banos O,Damas M,Guillen A,et al.Multi-sensor fusion based on asymmetric decision weighting for robust activity recognition[J].Neural Processing Letters,2015,42(1):5-26.
[14]Khaleghi B,Khamis A,Karray F O,et al.Multisensor data fusion:A review of the state-of-the-art[J].Information Fusion,2013,14(1):28-44.
[15]Safari S,Shabani F,Simon D.Multirate multisensor data fusion for linear systems using Kalman filters and a neural network[J].Aerospace Science and Technology,2014,39(1):465-471.
[16]Sasaoka T,Kimoto I,Kishimoto Y,et al.Multi-robot SLAM via information fusion extended Kalman filters[J].IFAC-PapersOnLine,2016,49(22):303-308.
[17]Houtekamer P L,Zhang F.Review of the ensemble Kalman filter for atmospheric data assimilation[J].Monthly Weather Review,2016,144(12):4489-4532.
[18]Sepasi S,Ghorbani R,Liaw B Y.Improved extended Kalman filter for state of charge estimation of battery pack[J].Journal of Power Sources,2014,255(1):368-376.
[19]Xiong Binyu,Zhao Jiyun,Wei Zhongbao.Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model[J].Journal of Power Sources,2014,262(1):50-61.
[20]Chambers A,Scherer S,Yoder L,et al.Robust multi-sensor fusion for micro aerial vehicle navigation in GPS-degraded/denied environments[C]//American Control Conference.Portland,OR,USA:IEEE,2014:1892-1899.
[21]Wen Tao,Tang Xianfeng,Ge Quanbo.Multi-sensor fusion based on unscented strong tracking information filter[C]//The 26th Chinese Control and Decision Conference(2014 CCDC).Changsha,China:IEEE,2014:370-374.
[22]Vercauteren T,Wang X.Decentralized sigma-point information filters for target tracking in collaborative sensor networks[J].IEEE Transactions on Signal Processing,2005,53(8):2997-3009.
[23]Lee D J.Unscented information filtering for distributed estimation and multiple sensor fusion[C]//AIAA Guidance,Navigation and Control Conference and Exhibit.Honolulu,Hawaii,USA:IEEE,2008:1-15.
[24]Gu Yu,Gross J N,Rhudy M B,et al.A fault-tolerant multiple sensor fusion approach applied to UAV attitude estimation[J].International Journal of Aerospace Engineering,2016(3):1-12.
[25]Caron F,Duflos E,Pomorski D,et al.GPS/IMU data fusion using multisensor Kalman filtering:Introduction of contextual aspects[J].Information Fusion,2006,7(2):221-230.
[26]Lu Jingyang,Niu R.False information injection attack on dynamic state estimation in multi-sensor systems[C]//2014 17th International Conference on Information Fusion(FUSION).Salamanca,Spain:IEEE,2014:1-8.
[27]Lu Jingyang,Niu Ruixin.Sparse attacking strategies in multi-sensor dynamic systems maximizing state estimation errors[C]//2016 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).Shanghai,China:IEEE,2016:3151-3155.
[28]Julier S J.The scaled unscented transformation[C]//Proceedings of the 2002 American Control Conference.Anchorage,Alaska,USA:IEEE,2002:4555-4559.
[29]赵琳.非线性系统滤波理论[M].北京:国防工业出版社,2012.
[30]Sibley G,Sukhatme G,Matthies L.The iterated sigma point Kalman filter with applications to long range stereo[C]//Proceedings of Robotics:Science and Systems.Philadelphia,USA:MIT Press,2006:1-6.
[31]Yang Chun,Mohammadi A,Chen Qingwei.Multi-sensor fusion with interaction multiple model and Chi-square test tolerant filter.[J].Sensors(Basel),2016,16(11):1835.

相似文献/References:

[1]陆敏智,李开明.一种6-THHT并联机器人位姿检测方法[J].南京理工大学学报(自然科学版),2008,(02):149.
 LU Min-zhi,LI Kai-ming.Position and Orientation Measurement of 6-THHT Parallel Robot[J].Journal of Nanjing University of Science and Technology,2008,(03):149.
[2]胡江华,柏连发,张保民.象素级多传感器图像融合技术[J].南京理工大学学报(自然科学版),1996,(05):73.
 Hu Jianghua Bai Lianfa Zhang Baomin.Pixel level Multisensor Image Fusion Technique[J].Journal of Nanjing University of Science and Technology,1996,(03):73.

备注/Memo

备注/Memo:
收稿日期:2016-12-26 修回日期:2017-01-20
基金项目:国家自然科学基金(61673219; 61673217)
作者简介:杨春(1989-),男,博士生,主要研究方向:组合导航、故障诊断、数据融合,E-mail:yangguang326@126.com; 通讯作者:陈庆伟(1963-),男,教授,博士生导师,主要研究方向:高精度轨迹跟踪、智能控制、导航制导,E-mail:cqw1002@sina.com。
引文格式:杨春,郭健,张磊,等.采用无迹信息滤波的多传感器容错融合算法[J].南京理工大学学报,2017,41(3):269-277.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-06-30