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Asynchronous Multisensor Data Fusion
多传感器异步数据融合算法

Guo Hui-dong,Zhang Xin-hua,Song Yuan,Lu Qiang-qiang,
郭徽东
,章新华,宋元,陆强强

电子与信息学报 , 2006,
Abstract: Due to the reasons of sensors in itself and the communication delays, the research of the asynchronous multisensor data fusion problem is more practical than that of synchronous one. Based on minimizing the trace of the fusion error covariance matrix, the asynchronous multisensor data fusion algorithm is presented. The multisensor track fusion is valid for asynchronous sensors as well as synchronous sensors. The algorithm makes up for the drawback that asynchronous fusion is limited to one-one sensors fusion. Significantly, the extensive multisensor asynchronous sensor fusion model is established. The analyse of algorithm in the theoretical deduction is presented. The format of algorithm is concise and applicable. Finally, simulation results show the asynchronous mulitsensor fusion is effective and the performance of the model is superior to that of single sensor.
Study on Multisensor Data Fusion Methods Based on Bayes Estimation
基于Bayes估计的多传感器数据融合方法研究

WU Xiao-jun,CAO Qi-ying,CHEN Bao-xiang,LIU Tong-ming,
吴小俊
,曹奇英,陈保香,刘同明

系统工程理论与实践 , 2000,
Abstract: In this paper, study is made on the multisensor data fusion methods. The optimal fused data is given by Bayes estimation theory, and optimal fused results obtained by other methods are compared with it.
THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION
杂波中多传感器数据融合改善目标航迹丢失的理论分析

Cui Ningzhou,Liu Yuan,Xie Weixin,
崔宁周
,刘源,谢维信

电子与信息学报 , 1999,
Abstract: The paper analyses the improvement of track loss in clutter with multisensor data fusion. By detemination of the transition probability desity function for the fusion prediction error, one can study the mechanism of track loss analytically. For nearest-neighbor association algorithm, the paper studies the fusion tracking performance parameters,such as mean time to lose fusion track and the fraction of lost fusion track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fusion tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.
STATE ESTIMATION FOR TWO-LEVEL HYBRID MULTISENSOR DATA FUSION SYSTEMS
两级混合多传感器信息融合中的状态估计

He You,Lu Dajin,Peng Yingning,
何友
,陆大

电子与信息学报 , 1999,
Abstract: In hybrid multisensor systems where a part of sensors processes their data locally to produce local tracks, another part of sensors only provides detection reports, the tracks and detection reports are communicated to a central site where track fusion and composite filtering are performed. This paper presents a globally optimal composite filtering solution for a two-level hybrid multisensor system. It is shown that the fusion center first needs to fuse the local estimates from the L sensors, and then to update recursively the fused track by using a Kalman filter based on the observations of the other N-L sensors. This paper also considers the estimation problems based on the different Cartesian coordinates.
Review of "Mathematical Techniques in Multisensor Data Fusion" by David L. Hall and Sonya A. H. McMullen
Ge Wang
BioMedical Engineering OnLine , 2005, DOI: 10.1186/1475-925x-4-23
Abstract: This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems.Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their appl
A Rough Neural Network Algorithm for multisensor Information Fusion
Xiaohui Chen
TELKOMNIKA : Indonesian Journal of Electrical Engineering , 2012, DOI: 10.11591/telkomnika.v10i6.1661
Abstract: The multisensor information fusion is a key issue for multisensor system. One of its difficulties lies in the switching of the state of sensor clusters. That is, which direction should the sensor information been fused into at a given moment? An algorithm of multisensor information fusion based on rough set and neural network was proposed in this paper. Firstly, the typical clustering distributions of 54 sensors within one day were regarded as sample space. The rough set was used for access of knowledge to make the decision table of the "data - fusion distribution". Next, the redundant properties and samples of information in one month were removed using the method of knowledge reduction of rough set. Then, the neural network was applied for clustering and analyzing to form the distribution rules of multisensor information fusion. Finally, the rough neural fusion algorithm, the neural quotient space fusion algorithm and word computing fusion algorithm are simulated and analyzed. The results show that the model and algorithm proposed in the paper are efficient in classification and rapid in sensor clustering distribution decide.
Multisensor Distributed Track Fusion Algorithm Based on Strong Tracking Filter and Feedback Integration
基于强跟踪滤波和反馈综合的多传感器分布式航迹融合

YANG Guo-Sheng,WEN Cheng-Lin,TAN Min,
杨国胜
,文成林,谭民

自动化学报 , 2004,
Abstract: A new multisensor distributed track fusion algorithm is put forward based on combining the feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gate is constructed by taking the intersection of the tracking gates formed before and after feedback. Secondly, on the basis of the constructed effective tracking gate, probabilistic data association and strong tracking Kalman filter are combined to form the new multisensor distributed track fusion algorithm. At last, simulation is performed on the original algorithm and the algorithm presented.
Inconsistency between WMAP data and released map
Hao Liu,TiPei Li
Chinese Science Bulletin , 2010, DOI: 10.1007/s11434-010-0131-5
Abstract: A remarkable inconsistency between the calibrated differential time-ordered data (TOD) of the Wilkinson Microwave Anisotropy Probe (WMAP) mission, which is the input for map-making, and the cosmic microwave background (CMB) temperature maps published by the WMAP team is revealed, indicating that there must exist a serious problem in the map making routine of the WMAP team. This inconsistency is easy to be confirmed without the use of WMAP map-making software. In view of the importance of this issue for cosmology study, we invite readers to check it by themselves.
Inconsistency between WMAP data and released map

Hao Liu,TiPei Li,

科学通报(英文版) , 2010,
Abstract: A remarkable inconsistency between the calibrated differential time-ordered data (TOD) of the Wilkinson Microwave Anisotropy Probe (WMAP) mission,which is the input for map-making,and the cosmic microwave background (CMB) temperature maps published by the WMAP team is revealed,indicating that there must exist a serious problem in the map making routine of the WMAP team.This inconsistency is easy to be confirmed without the use of WMAP map-making software.In view of the importance of this issue for cosmology...
Handling Uncertainty in Accessing Petroleum Exploration Data Traitement de l'incertain dans l'accès aux données d'exploration pétrolière  [cached]
Chung P. W. H.,Inder R.
Oil & Gas Science and Technology , 2006, DOI: 10.2516/ogst:1992021
Abstract: This paper discusses the role of uncertainty in accessing petroleum exploration databases. Two distinct forms of uncertainty are identified : the uncertainty in the user's requirements, and the uncertainty in the data held. A general mechanism is described which is applicable to both. Cet article traite du r le de l'incertitude dans l'accès aux bases de données d'exploration pétrolière. Deux sortes distinctes d'incertitudes sont identifiées : l'incertitude au niveau des requêtes de l'utilisateur et l'incertitude attachée aux données stockées. Nous décrivons un mécanisme général qui s'applique à ces deux types d'incertitude.
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