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多传感器信息融合系统有序度分析 被引量:3

The degree of order analysis of the multi-sensor information amalgamation system
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摘要 对多传感器信息融合系统,提出了融合处理的结构;通过引入信息融合系统有序度的概念,建立了一种基于信息熵理论的结构体系评价方法,用以从传感器管理的角度评价多传感器融合体系结构的组织化程度. Focusing on the structure of multi-senor imformation fusion,this paper put forward a structure of fusion process. And by introducing the conception of degree of order to the multisensor information fusion system, a structural system assessment method is put forward, which can estimate the organizational degree of the multisensor information fusion system from the perspective of the sensor administrant.
出处 《四川大学学报(自然科学版)》 CAS CSCD 北大核心 2010年第1期81-84,共4页 Journal of Sichuan University(Natural Science Edition)
关键词 信息融合 有序度 组织化程度 information fusion,degree of order, organizational degree,entropy
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  • 1李习彬.熵-信息理论与系统工程方法论的有效性分析[J].系统工程理论与实践,1994,14(2):37-42. 被引量:82
  • 2邱菀华.熵学及其近代应用[M].北京:北京航空航天大学出版社,1993.15-35.
  • 3张雄伟.未来战场上的数字化旅[M].北京:解放军出版社,1999.
  • 4谢稀仁.计算机网络[M].北京:电子工业出版社,1994,10.
  • 5Wiley E O. Entropy and Evolution,Entropy,Information and Evolution[M]. Massachusetts Institute of Technology, 1998.
  • 6Layer D. Information in Cosmology,Physics and Biology[J] ,Int. J Quantum Chen, 1995,12:185-195.
  • 7李伟钢.复杂系统结构有序度——负熵算法[J].系统工程理论与实践,1988,8(4):15-22. 被引量:48

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  • 1康庆生,孟正大,戴先中.图像雅可比矩阵伪逆估计在视觉伺服中的应用[J].机器人,2006,28(4):406-409. 被引量:8
  • 2Corke P I, Hutchinson S A. A new partitioned ap- proach to image-based visual servo control[J]. IEEE Transaction on Robotics and Automation, 2001, 17 (4) : 507.
  • 3Hutchison S, Hager G D, Corke P I. A tutorial on visual servo control[J]. IEEE Transactions on Ro- botics and Automation, 1996, 12(5): 651.
  • 4Kang Q S, Hao T, Meng Z D, etal. Pseudo-inverse Estimation of Image Jaeobian Matrix in Uncalibrated Visual Servoing[C]//IEEE International Conference On Meehatronies&Automation. Luoyang, China: IEEE, 2006.
  • 5Azad S, Amir-massoud F, Martin J. Robust jaeobi- an estimation for unealibrated visual servoing[C]// IEEE International Conference on Robotics and Au- tomation. Anchorage, Alaska, USA: IEEE,2010.
  • 6Denoux T. Conjunctive and disjunctive combination of belief functions induced by non distinct bodies of evidence[J].Artificial Intelligence, 2008, 172 (2/ 3) : 234.
  • 7Yager R R. Comparing approximate reasoning andprobabilistic reasoning using the Dempster-Shafer framework[J-]. International Journal of Approximate Reasoning, 2009, 50(5): 812.
  • 8Yang J, Sen P. A general multi level evaluation process for hybrid MADM with uncertainty [J]. IEEE Transaction on System, Man and Cybernetics, 2006, 36 (10): 1458.
  • 9Zeng D H, Xu J M, Xu G. Data fusion for traffic in- cident detection using D-S evidence theory with prob- abilistic SVMs[J]. Journal of Computers, 2008, 3 (10) : 36.
  • 10Wu W Z. Attribute reduction based on evidence theo- ry in incomplete decision systemsI-J]. InformationSciences, 2008, 178(5)~ 1355.

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