多源不完善信息融合方法及其应用研究
|
摘要
信息融合技术是上个世纪70年代末发展而来的一门新学科,它最早是从军事上的C3I(Command, Control, Communication and Intelligence)和IW(Information Warfare)系统上发展而来的,但随着科学技术的飞速发展,特别是近十几年,信息融合技术由军事应用向民用迅速转化,其应用领域不断扩大。随着人类对未知领域认识的不断深入,对识别精度,准确度,融合控制的鲁棒性和实时性,融合模型建立的合理性,以及融合管理的高效性的要求不断提高。但由于传感设备的物理局限性,系统运行的不确定性,环境的未知动态干扰等导致的不完善信息融合问题日益成为信息融合技术研究的一大挑战。
信息融合理论方法的研究是解决多源不完善信息融合问题的有效途径,尽管近20年中提出很多信息融合的理论和方法,有助于不完善信息融合特定问题的解决,但实践证明现有的理论和方法都有一定的局限性,使其应用受到很大的限制。因此迫切需要提出一种广适方法来解决不完善信息的定量和定性融合问题。本文以DSmT(Dezert-Smarandache Theory)和中智理论为框架,以移动机器人在未知环境自主创建地图为背景,对多源不完善信息的广义融合展开了深入的研究。
本文从信息的不精确定量融合的角度研究出发,将模糊理论、中智理论与信度赋值技术进行关联,进一步扩展了信度赋值技术的组合规则,提高了广义融合机融合算子组合证据源的能力,扩大了不完善信息的广义融合范围。
在广义框架下,深入地研究信息的不确定定性融合问题,在改善定性运算算子的基础上,提出细化定性信度语义标签的思想,给出相应的EQB(Enrichment of Qualitative Belief)运算算子,定性合取、DST(Dempster-Shafer Theory)、DSmT组合规则,以及冲突分配规则,仿真计算表明:新提出的定性融合方法融合范围广,融合精度高,能够大大提高广义融合机融合算子组合证据源和冲突分配器分配冲突的能力。
从信度赋值技术的角度出发,在广义幂集空间下,提出衡量两个证据源之间接近程度的证据支持贴近度的思想,因此有助于选择基本一致证据源,减少信息融合计算的复杂度,提高了融合精度和准确性。
在广义幂集空间下,提出了由ESMS(Evidence Supporting Measurement of Similarity)信息过滤器、融合算子、冲突分配器构建广义融合机的方法,克服了错误、虚假和不一致等信息对融合机的干扰影响,充分发挥了融合机优势,减少融合计算的复杂度。进一步提高了不完善信息融合的准确性和精度,扩展了融合算子的融合空间和范围,提高了冲突分配的灵活性。
针对Sonar传感器获取信息的不确定,在快速Hough变换自定位的基础上,应用广义融合方法对结构化环境进行了在线栅格地图创建,并同其它信息融合方法(DSmT,DSmT耦合PCR5(Proportional Conflict Redistribution Rule No.5),概率论、模糊理论、DST和灰色系统理论)进行了比较,比较结果说明了新方法具有融合精度高、计算效率高、算法稳定性好,适用范围广等优点,为智能机器人的导航、路径规划、SLAM(Simultaneous Localization And Mapping)等研究提供了新的思路。
最后,本文基于VC++ 6.0和OpenGL设计开发了机器人智能融合感知系统,并且以Pioneer II移动机器人作为实验平台,验证了系统的可行性。该软件系统具有动态在线显示和在线控制的功能,以及面向对象模块化的设计风格,成为机器人感知和信息融合仿真与实验的平台。
Information fusion technology is a new subject, which was developed from the martial application at the end of 1970s. With the rapid development of science and technology, especially since ten years ago, it has also got extensive applications in unmilitary fields. Then higher requirements are needed in precision and correctness of recognition, robustness of real-time fusion control and high effecicies of fusion management. However, due to the physical limitation of sensors, uncertainty of system and dynamic disturbance, imperfect information fusion is becoming a challenging problem.
The researches on theories and methods are very useful to solve the problem of imperfect information fusion. Though many theories and methods have been proposed since 20 years ago, they can just deal with the special case in imperfect information fusion. Therefore, an extensive method needs to be proposed urgently to solve the quantitative and qualitative fusion problems. Here we take Dezert-Smarandache Theory (DSmT) and Neutrosophic theory regards as the framework, carry out studying deeply the generalized fusion problem of imperfect information from multi-source on the background of map building of mobile robot in unknown environment.
From the point of view of quantitative fusion of imprecise information,we associate Fuzzy theory and Neutrosophic theory with belief assignment technology, and extend the combinational rules of belief assignment technology. So we improve the ability of fusion operator in combining evidence sources, and expand the fusion scope.
Within the generalized framework, we study deeply on the qualitative fusion of uncertain information. We propose the idea of enrichment of qualitative belief (EQB) linguistic label on the basis of improving the qualitative operators, and also propose the corresponding EQB operator, EQB combinational rule (i.e. conjunctive rule, DSmT, etc), and EQB conflict redistribution rule. Some examples show that the method of qualitative combination has the advantage of extensive fusion scope, high fusion precision. The new method also improves greatly the ability in combining evidence sources and in redistributing conflict mass.
Considering the belief assignment technology, we propose the idea of ESMS within the generalized power-set space, which can weigh the similarity between two evidence sources. The ESMS functions are useful to select consistent evidential sources to combine, reduce the complexity of computation, and improve the precision and correctness of fusion.
We propose an approach to construct generalized fusion machine within the generalized power-set space, which consists of fusion operator, conflict redistribution, and information filter. Generalized fusion machine not only can avoid from the influence of mistaken, illusive and inconsistent information on fusion machine, exert the advantage of fusion machine adequately, and reduce the complexity of computation, and improve the effect of fusion greatly, but also improve the precision and correctness of fusion, expand the fusion scope of operator, and improve the flexibility of conflict redistribution.
Aiming at the uncertainty of acquiring information with sonar sensors, we apply generalized fusion machine to grid map building online of mobile robot in structured environment with the help of self-localization based on fast-Hough transform. At the same time we also compare the generalized fusion machine with other theories (i.e. Probability theory, Dempster–Shafer Theory (DST), Fuzzy theory, Gray theory,DSmT,DSmT coupling with proportional conflict redistribution No.5 (PCR5)) in building map. The results of comparison show the advantage of high fusion precision, high computation efficiency, good stability and extensive applicability etc. The new tool also proposes a new research approach for navigation, path planning, SLAM (Simultaneous Localization And Mapping) and so on.
At last, we design and develop an intelligent perception system for mobile robot based on Visual C++ 6.0 and OpenGL, and take Pioneer II mobile robot regards as the experimental platform, where the soft system is testified to be very valid. In addition, the system has the functions of dynamic display and control on-line and also has the design style of object-oriented. It becomes the platform of simulation and experiment for robot perception and information fusion.
信息融合理论方法的研究是解决多源不完善信息融合问题的有效途径,尽管近20年中提出很多信息融合的理论和方法,有助于不完善信息融合特定问题的解决,但实践证明现有的理论和方法都有一定的局限性,使其应用受到很大的限制。因此迫切需要提出一种广适方法来解决不完善信息的定量和定性融合问题。本文以DSmT(Dezert-Smarandache Theory)和中智理论为框架,以移动机器人在未知环境自主创建地图为背景,对多源不完善信息的广义融合展开了深入的研究。
本文从信息的不精确定量融合的角度研究出发,将模糊理论、中智理论与信度赋值技术进行关联,进一步扩展了信度赋值技术的组合规则,提高了广义融合机融合算子组合证据源的能力,扩大了不完善信息的广义融合范围。
在广义框架下,深入地研究信息的不确定定性融合问题,在改善定性运算算子的基础上,提出细化定性信度语义标签的思想,给出相应的EQB(Enrichment of Qualitative Belief)运算算子,定性合取、DST(Dempster-Shafer Theory)、DSmT组合规则,以及冲突分配规则,仿真计算表明:新提出的定性融合方法融合范围广,融合精度高,能够大大提高广义融合机融合算子组合证据源和冲突分配器分配冲突的能力。
从信度赋值技术的角度出发,在广义幂集空间下,提出衡量两个证据源之间接近程度的证据支持贴近度的思想,因此有助于选择基本一致证据源,减少信息融合计算的复杂度,提高了融合精度和准确性。
在广义幂集空间下,提出了由ESMS(Evidence Supporting Measurement of Similarity)信息过滤器、融合算子、冲突分配器构建广义融合机的方法,克服了错误、虚假和不一致等信息对融合机的干扰影响,充分发挥了融合机优势,减少融合计算的复杂度。进一步提高了不完善信息融合的准确性和精度,扩展了融合算子的融合空间和范围,提高了冲突分配的灵活性。
针对Sonar传感器获取信息的不确定,在快速Hough变换自定位的基础上,应用广义融合方法对结构化环境进行了在线栅格地图创建,并同其它信息融合方法(DSmT,DSmT耦合PCR5(Proportional Conflict Redistribution Rule No.5),概率论、模糊理论、DST和灰色系统理论)进行了比较,比较结果说明了新方法具有融合精度高、计算效率高、算法稳定性好,适用范围广等优点,为智能机器人的导航、路径规划、SLAM(Simultaneous Localization And Mapping)等研究提供了新的思路。
最后,本文基于VC++ 6.0和OpenGL设计开发了机器人智能融合感知系统,并且以Pioneer II移动机器人作为实验平台,验证了系统的可行性。该软件系统具有动态在线显示和在线控制的功能,以及面向对象模块化的设计风格,成为机器人感知和信息融合仿真与实验的平台。
Information fusion technology is a new subject, which was developed from the martial application at the end of 1970s. With the rapid development of science and technology, especially since ten years ago, it has also got extensive applications in unmilitary fields. Then higher requirements are needed in precision and correctness of recognition, robustness of real-time fusion control and high effecicies of fusion management. However, due to the physical limitation of sensors, uncertainty of system and dynamic disturbance, imperfect information fusion is becoming a challenging problem.
The researches on theories and methods are very useful to solve the problem of imperfect information fusion. Though many theories and methods have been proposed since 20 years ago, they can just deal with the special case in imperfect information fusion. Therefore, an extensive method needs to be proposed urgently to solve the quantitative and qualitative fusion problems. Here we take Dezert-Smarandache Theory (DSmT) and Neutrosophic theory regards as the framework, carry out studying deeply the generalized fusion problem of imperfect information from multi-source on the background of map building of mobile robot in unknown environment.
From the point of view of quantitative fusion of imprecise information,we associate Fuzzy theory and Neutrosophic theory with belief assignment technology, and extend the combinational rules of belief assignment technology. So we improve the ability of fusion operator in combining evidence sources, and expand the fusion scope.
Within the generalized framework, we study deeply on the qualitative fusion of uncertain information. We propose the idea of enrichment of qualitative belief (EQB) linguistic label on the basis of improving the qualitative operators, and also propose the corresponding EQB operator, EQB combinational rule (i.e. conjunctive rule, DSmT, etc), and EQB conflict redistribution rule. Some examples show that the method of qualitative combination has the advantage of extensive fusion scope, high fusion precision. The new method also improves greatly the ability in combining evidence sources and in redistributing conflict mass.
Considering the belief assignment technology, we propose the idea of ESMS within the generalized power-set space, which can weigh the similarity between two evidence sources. The ESMS functions are useful to select consistent evidential sources to combine, reduce the complexity of computation, and improve the precision and correctness of fusion.
We propose an approach to construct generalized fusion machine within the generalized power-set space, which consists of fusion operator, conflict redistribution, and information filter. Generalized fusion machine not only can avoid from the influence of mistaken, illusive and inconsistent information on fusion machine, exert the advantage of fusion machine adequately, and reduce the complexity of computation, and improve the effect of fusion greatly, but also improve the precision and correctness of fusion, expand the fusion scope of operator, and improve the flexibility of conflict redistribution.
Aiming at the uncertainty of acquiring information with sonar sensors, we apply generalized fusion machine to grid map building online of mobile robot in structured environment with the help of self-localization based on fast-Hough transform. At the same time we also compare the generalized fusion machine with other theories (i.e. Probability theory, Dempster–Shafer Theory (DST), Fuzzy theory, Gray theory,DSmT,DSmT coupling with proportional conflict redistribution No.5 (PCR5)) in building map. The results of comparison show the advantage of high fusion precision, high computation efficiency, good stability and extensive applicability etc. The new tool also proposes a new research approach for navigation, path planning, SLAM (Simultaneous Localization And Mapping) and so on.
At last, we design and develop an intelligent perception system for mobile robot based on Visual C++ 6.0 and OpenGL, and take Pioneer II mobile robot regards as the experimental platform, where the soft system is testified to be very valid. In addition, the system has the functions of dynamic display and control on-line and also has the design style of object-oriented. It becomes the platform of simulation and experiment for robot perception and information fusion.
引文
[1]潘泉,于昕,程咏梅等.信息融合理论的基本方法与进展.自动化学报, 2003, 29( 4): 599~615
[2] F. Smarandache. A unifying field in logics: neutrosophic logic, neutrosophy, neutrosophic set, probability, and statistics. American Research Press, Rehoboth, 2000, http://www.gallup.unm.edu/~smarandache/neutrosophy.htm
[3] F. Smarandache, J. Dezert (Editors), Advances and applications of DSmT for information fusion. American Research Press, Rehoboth, 2004, http://www.gallup. unm.edu/?smarandache/DSmT-book1.pdf
[4] E. Waltz, J. Linas MultisensorData Fusion. Boston, Artech House, 1990.
[5] Pohl, V. Genderen. Multi-sensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 1998, 19(5): 823~854
[6] M. Mangolini, Apport de la fusion d’images satellitaires multicapteursau niveau pixel en télédétection et photo-interprétation. [Thèse de doctorat]. Univ. Nice–Sophia Antipolis, France, 1994
[7] L. Hall, J. Llinas. An introduction to multisensor data fusion. Proc. IEEE, 1997, 85(1): 6~23
[8] U.S. Department of Defense, Data fusion sub-panel of the joint directors of laboratories, Technical Panel for C3,“Data fusion lexicon,”1991.
[9] L.A. Klein. Sensor and data fusion concepts and applications. SPIE Optical Engineering,1993,TT14:48~54
[10] X. Rong Li. Information Fusion for Estimation and Decision.in: proceedings of International Workshop on Data Fusion in 2002, Beijing.
[11] L. Wald. An European proposal for terms of reference in data fusion. International Archives of Photogrammetry and Remote Sensing, 1998 , XXXII (7): 651~654
[12] M. Bedworth, Jane O’Brien Jemity. The omnibus model: A new model of data fusion. in:Proceedings of The Sec. Int. Conference on Information Fusion. California, USA :Sunnyvale .1999. 337~345
[13] C. B. Frankel, M. D. Bedworth. Control, estimation and abstraction in fusion architectures : Lessions from human information processing. In :Proceedings of The3rd Int. Conference on Information Fusion. France: Paris. 2000. 130~137
[14] Elisa Shahbazian Dale E , Blodgett Paul Labbé. The extended OODA model for data fusion systems. In : Proceedings of The 4th Int. Conference on Information Fusion. Canada . 2001. 106~112
[15] P. Hannah. A. Starr. Decisions in condition monitoring -An examplar for data fusion architecture.in : Proceedings of The 3rd Int. Conference on Information Fusion. France: Paris. 2000. 291~298
[16] David, L Hall. An introduction to multisensor fusion. in: Proceedings of International workshop on data fusion. Beijing , China. 2002
[17] J. Llinas, C. Bowman, G.Rogova, et al. Revisiting the JDL data fusion model II. In: Proceedings of The 7th Int. Conf. on Information Fusion. Vol. 2, 2004. 1218~1230
[18] 21 ideas for the 21st century. Business Week. Aug. 30, 1999. 78~167
[19] Chee-yee Chong, Srikanta P. Kumar. Sensor Networks: Evolution, Opportunities, and Challenges. Proceedings of the IEEE, Augest 2003,91(8):1247~1256
[20] Sinha, A. Chandrakas. A dynamic power management in wireless sensor networks. IEEE Design & Test of Computers. 2001, 18 (2) :62 ~ 74
[21] S. Hedetniemi, A. Liestman. A survey of gossiping and broad casting in communication networks. Networks, 1998, 18 (4):319 ~349
[22] J. Kulik, W. R. Heinzelman, et al. Negotiation - based protocols for disseminating information in wireless sensor networks. Wireless Networks, 2002, 8 (8):169 ~185
[23]刘先省,申石磊,潘泉.传感器管理及方法综述.电子学报, 2002,30(3):395～398
[24]刘先省.传感器管理方法研究.[博士论文].西安:西北工业大学图书馆,2000.
[25] J. M. Nash. Optimal allocation of tracking resource.in: Proceedings of IEEE Conference on Decision and Control. 1977.1177~1180
[26] P. L Rothman, S. G. Bier. Evaluation of Sensor Management Systems. In:Proceedings of The IEEE 1989 National Aerospace and Electronics Conference. Vol.4, Dayton, OH. May, 22-26 1989. 1747~1752
[27] J. M. Manyika, H. Durrant-Whyte. On Sensor Management in Decentralized Data Fusion. In:Proceedings of The 31st Conference on Decision and Control. Vol.4, Tucson, AZ. December, 16-18 1992. 3506~3507
[28] B. D. Leon, P. R. Heller. An Expert System and Simulation Approach for Sensor Management and Control in Distributed Surveillance Network.In: Proceedings of theSPIE for Applications of Artificial Intelligence V -The International Society for Optical Engineering. Vol.786, Orlando FL. May, 18-20 1987. 41~51.
[29] Gaskell, P. Probert. Sensor Models and a Framework for Sensor Management. In:Proceedings of the SPIE for Sensor FusionVI-The International Society for Optical Engineering. Vol.2059, Boston, MA. September, 7-8 1993. 2~13
[30]程利民,孔力,李新德.信息融合方法及应用研究.传感器与微系统, 2007, 26(3):4~9
[31]杨兆升,杨庆芳,冯金巧.路段平均速度组合融合算法及其应用.吉林大学学报,2004,4: 675~ 6781
[32] P. J. Edwards, A.M. Peacock. Minimizing risk using prediction uncertainty in neural network estimation fusion and its application to papermaking. IEEE Transactions on Neural Networks, May 2002, 13(3): 726~731
[33]朱洪艳,韩崇昭,韩红等.分布式多传感信息融合系统的异步航迹关联方法.控制理论与应用,2004, 21(3): 453~462
[34] R. E. Kalman, A new approach to linear filter and prediction theory. J. Basic Engr, 1960,83D: 35~45
[35] K. Wang, Y. Zhuang, W. Wang. A comparison of information fusion methods for locating intelligent mobile robot.in: Procceedings of 2006 International Conference on Machine Learning and Cybernetics. Aug. 2006.1762~1767
[36] J. C. Hyland. An iterated-extended Kalman filter algorithm for tracking surface and sub-surface targets.in: Proceedings of Oceans '02 MTS/IEEE. Vol.3, Oct. 2002.1283~1290
[37] Brunn, F. Sawo, U.D. Hanebeck. Efficient Nonlinear Bayesian Estimation based on Fourier Densities.in: Procceedings of 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Sept. 2006. 317~ 322
[38]汪西莉,刘芳等.融合上下文信息的多尺度贝叶斯图像分割.计算机学报, 2005, 28(3): 386~391
[39] Kamberova, R. Mandelbaum, M. Mintz. Statistical decision theory for mobile robotics: theory and application. in: Procceedings of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems. Dec, 8-11 1996.17~ 24
[40] Dellaert, D. Fox, W. Burgard et al. Monte carlo localization for mobile robots.in: Procceedings of the IEEE Int. Conf. on Robotics and Automation.Michigan.1999.1322~1328
[41] Fox, W. Burgard, S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems,1998, 25(12):195~207
[42] M. R. Morelande, S. Challa. A multitarget tracking algorithm based on random sets.in: Proceedings of the Sixth International Conference Information Fusion.Vol.2, 2003.807~814
[43] M.Wenz, H.Worn. Event-based production rules for data aggregation in wireless sensor networks.in: Procceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 2006 Sept. 59 ~64
[44]王卫华,陈卫东,席裕庚.移动机器人地图创建中的不确定传感信息处理.自动化学报, 2003, 29(2): 267~274
[45] Oriolo, G. Ulivi, M. Vendittelli. Fuzzy maps: A new tool for mobile robot perception and planning. Journal of Robotic System,1997, 14(3): 179~197
[46]张启忠,蒋静坪.用于机器人信息融合RS智能系统.自动化学报, Sept, 2002, 128(15): 797~801
[47] G. Shafer. A mathematical theory of evidence. Princeton. N.J: Princeton University Press.1976
[48] P. Smets, R. Kennes. The transferable belief model. Artificial Intelligence, 1994, 66(2): 191~234
[49]郭惠勇,张陵.基于遗传算法和加权D-S信息融合的结构多损伤位置识别.机械工程学报, 2004, 40(9): 148~153
[50] W. Liu, K. Wang, M. Tang. Study on power System load forecasting based on MPSO artificial neural networks.in: Procceeding,The Sixth World Congress on Intelligent Control and Automation. Vol. 1, 2006. 2728 ~ 2732
[51] P. Dodin, J. Verliac, V. Nimier. Analysis of the multisensor multitarget tracking resource allocation problem. in: Procceeding of The 3rd Int. Conf. on Information Fusion. France: Paris. Vol.2, July 2000. WEC1/17 ~ WEC1/22.
[52] X. Yang, W. Yang, J. Pei. Different focus points images fusion based on wavelet decomposition in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. Vol. 1, 2000. MOD3/3~8
[53] Robert, W. Burch. Semeiotic data fusion.in: Procceeding, The 3rd Int. Conf. on Information Fusion. France: Paris. Vol.2, July 2000. WEC4/11 - WEC4/16
[54]陈豪,俞能海,刘政凯,张荣.面向提高图像分辨率的遥感数据融合新算法.软件学报,2001, 12(10): 1534~1539
[55] Nguyan, V. Krainowich. Chu spaces - A new approach to diagnostic information fusion.in:Procceeding of The 2rd Int. Conf. on Information Fusion, California. USA : Sunnyvale. Vol.2,1999. 904~909
[56] Torrens , F. Quimica. Resolution enhancement with nonlinear gradient filtering.in: Procceeding of The 2rd Int. Conf. on Information Fusion, California , USA : Sunnyvale , Vol.1, 1999. 29~38
[57] S. Kendal, X. Chen, A. Masters. HyM: A methodology for the development of integrated hybrid intelligent information systems. in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. 2000. 871~876
[58] S. DeLoach , M. Kokar. Category theory approach to fusion of wavelet based features. in: Procceeding of The 2rd Int. Conf. on Information Fusion. USA : Sunnyvale,1999. 610~617
[59] W. Ross , A. Waxman, et al. Multi Sensor 3D Image Fusion and Interactive Search.in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. 2000. 501~508
[60] Rhodes, D. Luenberger. Differential games with imperfect state information. IEEE Transactions on Automatic Control, Feb 1969, 14(1): 29 ~38
[61] D. Dubois, H. Prade. Possibility Theory: An approach to the computerized processing of uncertain. New York: Plenum Press, 1988.
[62] P. Bosc, H. Prade. An introduction to fuzzy set and possibility theory based approaches to the treatment of uncertainty and imprecision in database management systems.in: Proceedings of The Sec. Workshop Uncertainty Management in Information Systems: from Needs to Solutions, Catalina,Calif.,1993.
[63] S. Post, P. Sage. An overview of automated reasoning. IEEE Transactions on Systems, Man, and Cybernetics, 1990, 20(1): 202~224
[64] S. Parsons. Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(3): 353~369
[65] M. F. Worboys, E. Clementini. Integration of imperfect spatial information. Journal of Visual languages and computing, 2001, 12:61~80
[66] Bordogna, S. Chiesa, D. Geneletti. Linguistic modeling of imperfect spatial information as a basis for simplifying spatial analysis. Information Science, 2006, 176:366~389.
[67] H. Wang, F. Smarandache, Y. Zhang and R. Sunderraman. Interval neutrosophic sets and logic: theory and applications in computing, Hexis, Arizona, 2005. Web available: http:// www.gallup.unm.edu/~smarandache /eBooks-otherformats.htm.
[68] J. Dezert, A. Tchamova, T.Semerdjiev, P. Konstantinova. Performance evaluation of fusion rules for multitarget tracking in clutter based on generalized data association .in: Procceeding of the 8th Int. Conf. on Information Fusion. USA: Philadelphia, July 2005. 2:1~8.
[69] R. Khedam, A. Bouakache, G. Mercier, A. Belhadj-Aissa. Improvement of Land Cover Map from Satellite Imagery using DST and DSmT. in: Procceeding of Information and Communication Technologies, 2006. April 2006. 1:383~388
[70] S. Bhattacharya, Utility, Rationality and Beyond - From Finance to Informational Finance. [ph.D dissertation], Bond University, Queensland, Australia, 2004.
[71] Elfes, H. Moravec. High resolution maps from wide angle sonar.in: Proceedings of IEEE Int Conf Robotics and Automation, 1985:116~121
[72] Elfes. Using occupancy grids for mobile robot perception and navigation. Computer, 1989, 22(6): 46~57
[73] Konolige. Improved occupancy grids for map building. Autonomous Robots, 1997, 4:351~367
[74] P. Arun, Tirumalai et al. Evidential reasoning for building environment maps. IEEE Transactions on System, Man, and Cybernetics,1995, 25(1): 10~20
[75] P. Daniel et al. An evidential approach to probabilistic map-building. in: Proceedings, IEEE Int. Conf. on Robotics and Automation. Minneapolis, Minnesota. April, 1996.745~750
[76]苏丽颖,曹志强,王硕等.多机器人对未知环境进行实时在线探测的一种方法.高技术通讯,2003,11:56~60
[77] R.HoseinNezhad, B.Moshirl, M. R. Asharif. Sensor fusion for ultrasonic and laser arrays in mobile robotics: A compareative study of fuzzy,Dsmpster and Bayesian approaches. in: Procceeding of Sensors 2002 by IEEE. Vol. 2, 12-14 June 2002. 1682 ~1689
[78]王卫华.未知环境中移动机器人创建地图研究.[博士论文].上海:上海交通大学图书馆,2003.
[79] J. J. Leonard, F. H. Durrant-White. Mobile robot localization by tracking geometric beacons. IEEE Transactions robotics and Automation. 1991, 7(30):376~382
[80] E. Wan, R. Merwe. The unscented kalman–filter for nonlinear estimations. in:Proceedings of IEEE Symposium (AS-SPCC). 2000.153~158
[81] W. Burgard, D. Fox, D. Hennig, T. Schmidt. Estimating the absolute position of a mobile robot using position probability grids.in: Procceeding of The National Conference on Artificial Intelligence (AAAI-1996). Oregon. 1996. 896~901.
[82] R. Ueda, T. Filase, Y. Kobayashi et al. Uniform Monte Carlo localization-fast and robust self-localization method for mobile robots.in: Proceedings of The 2002 IEEE International Conference on Robotics & Automation . Vol.5, 2002. 1353~1355
[83] J. S. Gutmann, W. Brugard, D. Fox. An experimental comparison of localization methods.in: Proceedings of IEEE International Conf on Intelligent Robots and Systems. Victoria, Canada. 1998. 736 ~743.
[84]房芳,马旭东,戴先中.基于霍夫空间模型匹配的移动机器人全局定位方法.机器人,2005, 27(1): 35~40
[85]尚文,马旭东,戴先中.融合多传感器信息的移动机器人自定位方法.东南大学学报(自然科学版) , 2004, 34(6): 784~788
[86] Zadeh, L. Fuzzy sets. Information and Control, 1965, 8(3):338~353
[87] D. Dubois and H. Prade. Théorie des possibilités: Applicationàla représentation des connaissances en informatique. Masson, Paris, 1985
[88] F. Smarandache.ed. Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, Gallup Campus, NM: Univ. of New Mexico, 1-3 December 2001
[89] Ashbacher. Introduction to neutrosophic logic. American Research Press, 2002.
[90] L Zadeh. On the validity of Dempster’s rule of combination. Memo M 79/24, Univ. of California, Berkeley, 1979.
[91] Zadeh. Review of mathematical theory of evidence. AI Magazine, 1984, 5(3): 81~83
[92] Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combination. Berkeley Cognitive Science Report No. 33, University of California, Berkeley, CA,1985
[93] L. Zadeh, A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine, 1986, 7(2): 85~90
[94] Dubois, H. Prade. On the unity of Dempster rule of combination, International Journal of Intelligent Systems, 1986, 1: 133~142
[95] F. Voorbraak. On the justification of Dempster’s rule of combination. Artificial Intelligence, 1991, 48:171~197
[96] J. Pearl. Reasoning with belief functions: an analysis of compatibility. International Journal of Approximate Reasoning, 1990, 4:363~389
[97] Dubois, H. Prade. A set-theoretic view of belief functions. International Journal of General Systems, 1986, 12:193~226
[98] Dubois, H. Prade, Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 1988, 4: 244~264
[99] P. Smets. Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate reasoning, 1993, 9: 1~35
[100] C. K. Murphy. Combining belief functions when evidence conflicts. Decision Support Systems, 2000, 29: 1~9
[101] R. R. Yager. On the relationships of methods of aggregation of evidence in expert systems. Cybernetics and Systems. 1985, 16: 1~21
[102] Klawonn, P. Smets. The dynamic of belief in the Transferable Belief Model and specialization generalization matrices. Uncertainty in Artificial Intelligence 92. Eds.D. Dubois, M. P. Wellman, B. D’Ambrosio, P. Smets, Morgan Kaufman, San Mateo, CA, 1992,130~137
[103] R. R. Yager. Hedging in the combination of evidence. Journal of Information and Optimization Science, 1983, 4(1): 73~81
[104] R. R. Yager. On the Dempster-Shafer framework and new combination rules. Information Sciences, 1987, 41: 93~138
[105] F. Smarandache. Unification of fusion theories (UFT). International Journal of Applied Mathematics & Statistics, 2004, 2: 1~14
[106] J. Dezert, F. Smarandache. On the generation of hyper-power sets for the DSmT.in: Proceedings of The 6th Int. Conf. Information Fusion. Australia: Cairns, Queensland.8-11, 2003. 1118-1125
[107] L. Comtet, Sperner Systems, sec.7.2 in Advanced Combinatories: The Art of Finiteand Infinite Expansions, D. Reidel Publ. Co.1974, 271~273
[108] R. Dedekind.über Zerlegungen von Zahlen durch ihre gr?ssten gemeinsammen Teiler, In Gesammelte Werke, Bd. 1. 1897, 103-148
[109] P. Smets. Data fusion in the transferable belief model. in: Proceedings of The 3rd International Conference on Information Fusion. Paris. July 10-13, 2000. PS21~PS33
[110] E. Lefevre, O. Colot, P. Vannoorenberghe. Belief functions combination and conflict management. Information Fusion Journal, 2002, 3(2):149~162
[111] L. Cheng, L. Kong, X. Li. Solving information fusion problems on unreliable evidential sources with generalized DSmT. Journal of Applied Sciences, 2006, 6(7): 1581~1585
[112] F. Smarandache(刘峰译).逻辑学的统一:中智逻辑中智学,中智集合论,中智概率论. Xiquan Publishing House, Chinese Branch, 2003. Web available: http://www.gallup.unm.edu/~smarandache/neutrosophy.htm
[113] T. Den?ux. Reasoning with Imprecise Belief Structures. International Journal of Approximate Reasoning, 1999, 20:79~111,
[114] T. Den?ux. Modeling Vague Beliefs Using Fuzzy-valued Belief Strictures. Fuzzy sets and Systems, 2000, 116:167-199
[115] F. Smaradache and J. Dezert. The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference. Web available: http://arxiv.org/PS_cache/cs/pdf /0412/0412091. pdf.
[116] T. Wagner, U. Visser, O. Herzog. Egocentric qualitative spatial knowledge representation for physical robots. Robotics and Autonomous Systems, 2004, 49(1-2): 25~42
[117] P. Ranganathan, J.B. Hayet, M. Devy et al. Topological navigation and qualitative localization for indoor environment using multi-sensory perception. Robotics and Autonomous Systems. 2002, 41,(2-3): 137~144
[118] Duckham, J. Lingham, K. Mason and M. Worboys. Qualitative reasoning about consistency in geographic information. Information Sciences, 2006, 176(6): 601~627
[119] Polya. Patterns of Plausible Inference. Princeton University Press, Princeton, NJ, 1954.
[120] L. Zadeh. A Theory of Approximate Reasoning. Machine Intelligence, 1979, 9:149~194
[121] L. Zadeh. Fuzzy logic = Computing with words. IEEE Transactions on Fuzzy Systems, 1996, 4(2): 103~111.
[122] M. P. Wellman. Some varieties of qualitative probability.in: Procceeding of The 5th Int. Conf. on Information Processing and the Management of Uncertainty (IPMU). Paris. July, 1994.
[123] S. Parsons and E. Mamdani. Qualitative Dempster - Shafer theory.in: Procceeding of the 3rd EMACS Int. Workshop on Qualitative Reasoning and Decision Technologies. Spain: Barcelona.1993.
[124] S. Parsons. Some qualitative approaches to applying Dempster-Shafer theory. Information and Decision Technologies, 1994, 19: 321~337
[125] S. Parsons. A proof theoretic approach to qualitative probabilistic reasoning. Int. J. of Approx. Reasoning, 1998, 19(3-4): 265~297
[126] Brewka, S. Benferhat and D.L. Berre. Qualitative choice logic, Artificial Intelligence, August 2004, 157(1-2): 203~237
[127] F. Smarandache, J. Dezert (Editors). Advances and Applications of DSmT for Information Fusion (Collected works). Vol.2, American Research Press, Rehoboth, 2006. Web available: http://www.gallup.unm.edu/ ?smarandache/DSmT-book2.pdf.
[128] B. Tessem.. Approximations for efficient computation in the theory of evidence. Artificial Intelligence, 1993, 61:315-329
[129] A. L. Jousselme, D. Grenier et al. A new distance between two bodies of evidence, Information fusion, 2001,2:91-101
[130] Diaz, M. Rifqi, B. Bouchon-Meunier, A Similarity Measure between Basic Belief Assignments.in: Procceeding of The 9th International Conference on Information Fusion. Italy: Florence. July 10-13, 2006.1~6
[131] B. Ristic. P. Smets. Association of Uncertain Combat ID Declarations. In: Procceeding of Cogis06 Conf.. Paris. March 2006.
[132] X. Li, J. Dezert, X. Huang. Selection of Sources as a prerequisite for Information Fusion with Application to SLAM.in: Procceeding, The 9th International Conference on Information Fusion. Italy: Florence. July 10-13, 2006.1~8
[133] Papoulis. Probability, Random Variables and Stochastic Processes. McGraw Hill, 4th Revised Edition, 2002.
[134] J. Dezert, Foundations for a new theory of plausible and paradoxical reasoning.International Journal Information and Security, 2002, 9:13-57
[135] Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distributions. IBull. Calcutta Math. Soc.,1943, 35: 99~109
[136] T. Kailath. The Divergence and Bhattacharyya Distance Measure in Signal Selection. IEEE Transactions on Communication Technology,1967 Com-15 1:52~60
[137] J. Dezert, F. Smarandache. Partial ordering of hyper-powersets and matrix representation of belief functions within DSmT.in: Proceedings of The 6th Int. Conf. on Information Fusion. Australia: Cairns, Queensland. 8-11 July 2003. 1230~2003
[138] J?sang, M. Daniel, P. Vannoorenberghe. Strategies for combing conflicting dogmatic beliefs.in: Proceedings of The 6th Int. Conf. on Information Fusion. Australia: Cairns, Queensland. Vol2, 8-11 July 2003. 1133~1140
[139] M. Daniel. Distribution of contradictive belief masses in combination of belief functions. Information, Uncertainty and Fusion, Chapter, Eds. B. Bouchon-Meunier, R.R.Yager, L.A Zadeh., Kluwer Academic Publishers, 2000, 431~446
[140] F. Smarandache, J. Dezert. Proportional conflict redistribution rules for information fusion. Applications and Advances of DSmT for Information Fusion (Collected works). Chapter, American Research Press, Rehoboth, Vol.2, 2006. Web available: http://www.gallup.unm.edu/?smarandache /DSmT-book2.pdf.
[141] F. Smarandache, J. Dezert. A Simple Proportional Conflict Redistribution Rule. International Journal of Applied Mathematics & Statistics,2005,3 (J05):1~36
[142] Martin, C. Osswald. A new generalization of the proportional conflict redistribution rule stable in terms of decision, Advances and Applications of DSmT for Information Fusion, Chapter, Amer. Res. Press, Rehoboth, 2006, http://www.gallup.unm.edu/ ~smarandache/DSmTbook2.pdf.
[143] G. Dissanayake, P. M. Newman et al. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robotics and Automation, 2001, 17(3): 229~241
[144] M. Montemerlo, S. Thrun. Simultaneous localization and mapping with unknown data association using Fast-SLAM. in: Proceedings of The IEEE int. conf. on Robotics and Automation. 2003.1985~1991.
[145] J. Dasvison, D. W. Murray. Simultaneous localization and mapping using active vision. IEEE Transactions on Pattern Analysis Machine Intelligence, 2002,24(7):865~880
[146] S. Thrun, D. Fox, W. Burgard et al. Robust monte carlo localization for mobile robots. Artificial Intelligence, 2001, 128: 99~141
[147] Castellanos, J.M.M. Montiel, J. Neira et al. Sensor influence. the performance of simultaneous mobile robot localization and map building. Eds. P. Corke and J. Trevelyan, editors, Experimental Robotics IV, Springer-Verlag, 2000: 287~296
[148] Elfes. Sonar-based real-world mapping and navigation. IEEE Journal of Robotics Automat, 1987, 3: 249~265
[149] Ofir Cohenand, Yael Edan. Adaptive fuzzy logic algorithm for grid-map based sensor fusion. in:Proceedings of 2004 IEEE Intelligent Vehicles Symposium. Italy. June 14-17 2004.625~630
[150] Baltzakis, P. Trahanias. An iterative approach for building feature maps in cyclic environments. in: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System.30 Sept.-5 Oct. Vol.1, 2002. 576 ~ 581
[151] G.A. Borges, M.J. Aldon. Optimal mobile robot pose estimation using geometrical maps.IEEE Transactions on Robotics and Automation, Feb. 2002,18(1):87~ 94
[152] Bandera, C. Urdiales, F. Sandoval. A hierarchical approach to grid-based and topological maps integration for autonomous indoor navigation. in: Proceedings of 2001 IEEE/RSJ International Conference onIntelligent Robots and Systems. Vol.2, 29 Oct.-3. Nov. 2001. 883 ~ 888.
[153] V. Egido, R. Barber, M.J.L Boada et al. Self-generation by a mobile robot of topological maps of corridors. in: Proceedings of IEEE International Conference on Robotics and Automation. Vol.3, 11-15 May 2002. 2662 ~ 2667
[154] E. Rybski, F.Zacharias, J. F Lett. Using visual features to build topological maps of indoor environments. in: Proceedings of IEEE International Conference on Robotics and Automation. Vol.1, 14-19 Sept. 2003. 850 ~ 855
[155]庄严,徐晓东,王伟.移动机器人几何一拓扑混合地图的构建及自定位研究.控制与决策, 2005, 20(7):815~822。
[156] Jensfelt,H. I. Christensen.Pose Tracking Using Laser Scanning and Minimalistic Environmental Models. IEEE Trans on Robotics and Automation,2001,17(2):138~147.
[157] N. Tomatis, I. Nourbakhsh, R. Siegwart. Hybrid Simultaneous Localization andMap Building:A Natural Integration of Topological and Metric.Robotics and Autonomous Systems,2003,44(1):3~14.
[158] X. Li, X. Huang, J. Dezert, et al. A successful application of DSmT in sonar grid map building and comparison with DST-based approach.International Journal of Innovative Computing, Information and Control, 2007,3(3)
[159] X. Li, X. Huang, M. Wang. Sonar grid map building of mobile robots based on DSmT. Information Technology Journal, 2006, 5(2):267~272
[160]李新德,黄心汉,王敏.基于信度赋值技术的统一融合框架(UFA)研究.华中科技大学学报, 2007,35 (01):46~49
[161]李新德,黄心汉, Jean Dezert.基于ESMS过滤器的信息融合理论研究及SLAM应用.计算机科学, 2006,33(12):117~122
[162]叶涛等.全局环境未知时机器人导航和避障的一种新方法.机器人,2003, 25(6) :516~520
[163] Cai, t. Fukuda et al. Integration of distributed sensing information in DARS based on evidential reasoning. in:Proceedings of The 3rd International Symposium on Distributed Autonomous Robotic Systems.1996.268~279
[164] S. Thrun, D. Fox, W.Burgard. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning, 1998, 31(1-3) :29~53
[165] C.F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions On Robotics and Automation, 2000, 16(1):55~66
[166] Saffiotti. The use of fuzzy logic in autonomous robot navigation: a catalogue raisonne. http://irida.ulb.ac.be/FLAR/survey.html
[167]王卫华,陈卫东,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人, 2001, 23( 6): 563~568
[168] X. Li, X. Huang, M. Wang. Robot map building from sonar sensors and DSmT. INFORMATION & SECURITY. An International Journal, 2006, 20: 104~121
[169] D. Fox. Adapting the sample size in particle filters through KLD-Sampling. International Journal of Robotic Research, 2003, 22(2):985-1004.
[170]贾志刚.精通OpenGL.(第1版).北京:电子工业出版社, 1998年08月
[171]乔林费广正等. OpenGL程序设计. (第1版).北京:清华大学出版社, 2000年4月
[2] F. Smarandache. A unifying field in logics: neutrosophic logic, neutrosophy, neutrosophic set, probability, and statistics. American Research Press, Rehoboth, 2000, http://www.gallup.unm.edu/~smarandache/neutrosophy.htm
[3] F. Smarandache, J. Dezert (Editors), Advances and applications of DSmT for information fusion. American Research Press, Rehoboth, 2004, http://www.gallup. unm.edu/?smarandache/DSmT-book1.pdf
[4] E. Waltz, J. Linas MultisensorData Fusion. Boston, Artech House, 1990.
[5] Pohl, V. Genderen. Multi-sensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 1998, 19(5): 823~854
[6] M. Mangolini, Apport de la fusion d’images satellitaires multicapteursau niveau pixel en télédétection et photo-interprétation. [Thèse de doctorat]. Univ. Nice–Sophia Antipolis, France, 1994
[7] L. Hall, J. Llinas. An introduction to multisensor data fusion. Proc. IEEE, 1997, 85(1): 6~23
[8] U.S. Department of Defense, Data fusion sub-panel of the joint directors of laboratories, Technical Panel for C3,“Data fusion lexicon,”1991.
[9] L.A. Klein. Sensor and data fusion concepts and applications. SPIE Optical Engineering,1993,TT14:48~54
[10] X. Rong Li. Information Fusion for Estimation and Decision.in: proceedings of International Workshop on Data Fusion in 2002, Beijing.
[11] L. Wald. An European proposal for terms of reference in data fusion. International Archives of Photogrammetry and Remote Sensing, 1998 , XXXII (7): 651~654
[12] M. Bedworth, Jane O’Brien Jemity. The omnibus model: A new model of data fusion. in:Proceedings of The Sec. Int. Conference on Information Fusion. California, USA :Sunnyvale .1999. 337~345
[13] C. B. Frankel, M. D. Bedworth. Control, estimation and abstraction in fusion architectures : Lessions from human information processing. In :Proceedings of The3rd Int. Conference on Information Fusion. France: Paris. 2000. 130~137
[14] Elisa Shahbazian Dale E , Blodgett Paul Labbé. The extended OODA model for data fusion systems. In : Proceedings of The 4th Int. Conference on Information Fusion. Canada . 2001. 106~112
[15] P. Hannah. A. Starr. Decisions in condition monitoring -An examplar for data fusion architecture.in : Proceedings of The 3rd Int. Conference on Information Fusion. France: Paris. 2000. 291~298
[16] David, L Hall. An introduction to multisensor fusion. in: Proceedings of International workshop on data fusion. Beijing , China. 2002
[17] J. Llinas, C. Bowman, G.Rogova, et al. Revisiting the JDL data fusion model II. In: Proceedings of The 7th Int. Conf. on Information Fusion. Vol. 2, 2004. 1218~1230
[18] 21 ideas for the 21st century. Business Week. Aug. 30, 1999. 78~167
[19] Chee-yee Chong, Srikanta P. Kumar. Sensor Networks: Evolution, Opportunities, and Challenges. Proceedings of the IEEE, Augest 2003,91(8):1247~1256
[20] Sinha, A. Chandrakas. A dynamic power management in wireless sensor networks. IEEE Design & Test of Computers. 2001, 18 (2) :62 ~ 74
[21] S. Hedetniemi, A. Liestman. A survey of gossiping and broad casting in communication networks. Networks, 1998, 18 (4):319 ~349
[22] J. Kulik, W. R. Heinzelman, et al. Negotiation - based protocols for disseminating information in wireless sensor networks. Wireless Networks, 2002, 8 (8):169 ~185
[23]刘先省,申石磊,潘泉.传感器管理及方法综述.电子学报, 2002,30(3):395～398
[24]刘先省.传感器管理方法研究.[博士论文].西安:西北工业大学图书馆,2000.
[25] J. M. Nash. Optimal allocation of tracking resource.in: Proceedings of IEEE Conference on Decision and Control. 1977.1177~1180
[26] P. L Rothman, S. G. Bier. Evaluation of Sensor Management Systems. In:Proceedings of The IEEE 1989 National Aerospace and Electronics Conference. Vol.4, Dayton, OH. May, 22-26 1989. 1747~1752
[27] J. M. Manyika, H. Durrant-Whyte. On Sensor Management in Decentralized Data Fusion. In:Proceedings of The 31st Conference on Decision and Control. Vol.4, Tucson, AZ. December, 16-18 1992. 3506~3507
[28] B. D. Leon, P. R. Heller. An Expert System and Simulation Approach for Sensor Management and Control in Distributed Surveillance Network.In: Proceedings of theSPIE for Applications of Artificial Intelligence V -The International Society for Optical Engineering. Vol.786, Orlando FL. May, 18-20 1987. 41~51.
[29] Gaskell, P. Probert. Sensor Models and a Framework for Sensor Management. In:Proceedings of the SPIE for Sensor FusionVI-The International Society for Optical Engineering. Vol.2059, Boston, MA. September, 7-8 1993. 2~13
[30]程利民,孔力,李新德.信息融合方法及应用研究.传感器与微系统, 2007, 26(3):4~9
[31]杨兆升,杨庆芳,冯金巧.路段平均速度组合融合算法及其应用.吉林大学学报,2004,4: 675~ 6781
[32] P. J. Edwards, A.M. Peacock. Minimizing risk using prediction uncertainty in neural network estimation fusion and its application to papermaking. IEEE Transactions on Neural Networks, May 2002, 13(3): 726~731
[33]朱洪艳,韩崇昭,韩红等.分布式多传感信息融合系统的异步航迹关联方法.控制理论与应用,2004, 21(3): 453~462
[34] R. E. Kalman, A new approach to linear filter and prediction theory. J. Basic Engr, 1960,83D: 35~45
[35] K. Wang, Y. Zhuang, W. Wang. A comparison of information fusion methods for locating intelligent mobile robot.in: Procceedings of 2006 International Conference on Machine Learning and Cybernetics. Aug. 2006.1762~1767
[36] J. C. Hyland. An iterated-extended Kalman filter algorithm for tracking surface and sub-surface targets.in: Proceedings of Oceans '02 MTS/IEEE. Vol.3, Oct. 2002.1283~1290
[37] Brunn, F. Sawo, U.D. Hanebeck. Efficient Nonlinear Bayesian Estimation based on Fourier Densities.in: Procceedings of 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Sept. 2006. 317~ 322
[38]汪西莉,刘芳等.融合上下文信息的多尺度贝叶斯图像分割.计算机学报, 2005, 28(3): 386~391
[39] Kamberova, R. Mandelbaum, M. Mintz. Statistical decision theory for mobile robotics: theory and application. in: Procceedings of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems. Dec, 8-11 1996.17~ 24
[40] Dellaert, D. Fox, W. Burgard et al. Monte carlo localization for mobile robots.in: Procceedings of the IEEE Int. Conf. on Robotics and Automation.Michigan.1999.1322~1328
[41] Fox, W. Burgard, S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems,1998, 25(12):195~207
[42] M. R. Morelande, S. Challa. A multitarget tracking algorithm based on random sets.in: Proceedings of the Sixth International Conference Information Fusion.Vol.2, 2003.807~814
[43] M.Wenz, H.Worn. Event-based production rules for data aggregation in wireless sensor networks.in: Procceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 2006 Sept. 59 ~64
[44]王卫华,陈卫东,席裕庚.移动机器人地图创建中的不确定传感信息处理.自动化学报, 2003, 29(2): 267~274
[45] Oriolo, G. Ulivi, M. Vendittelli. Fuzzy maps: A new tool for mobile robot perception and planning. Journal of Robotic System,1997, 14(3): 179~197
[46]张启忠,蒋静坪.用于机器人信息融合RS智能系统.自动化学报, Sept, 2002, 128(15): 797~801
[47] G. Shafer. A mathematical theory of evidence. Princeton. N.J: Princeton University Press.1976
[48] P. Smets, R. Kennes. The transferable belief model. Artificial Intelligence, 1994, 66(2): 191~234
[49]郭惠勇,张陵.基于遗传算法和加权D-S信息融合的结构多损伤位置识别.机械工程学报, 2004, 40(9): 148~153
[50] W. Liu, K. Wang, M. Tang. Study on power System load forecasting based on MPSO artificial neural networks.in: Procceeding,The Sixth World Congress on Intelligent Control and Automation. Vol. 1, 2006. 2728 ~ 2732
[51] P. Dodin, J. Verliac, V. Nimier. Analysis of the multisensor multitarget tracking resource allocation problem. in: Procceeding of The 3rd Int. Conf. on Information Fusion. France: Paris. Vol.2, July 2000. WEC1/17 ~ WEC1/22.
[52] X. Yang, W. Yang, J. Pei. Different focus points images fusion based on wavelet decomposition in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. Vol. 1, 2000. MOD3/3~8
[53] Robert, W. Burch. Semeiotic data fusion.in: Procceeding, The 3rd Int. Conf. on Information Fusion. France: Paris. Vol.2, July 2000. WEC4/11 - WEC4/16
[54]陈豪,俞能海,刘政凯,张荣.面向提高图像分辨率的遥感数据融合新算法.软件学报,2001, 12(10): 1534~1539
[55] Nguyan, V. Krainowich. Chu spaces - A new approach to diagnostic information fusion.in:Procceeding of The 2rd Int. Conf. on Information Fusion, California. USA : Sunnyvale. Vol.2,1999. 904~909
[56] Torrens , F. Quimica. Resolution enhancement with nonlinear gradient filtering.in: Procceeding of The 2rd Int. Conf. on Information Fusion, California , USA : Sunnyvale , Vol.1, 1999. 29~38
[57] S. Kendal, X. Chen, A. Masters. HyM: A methodology for the development of integrated hybrid intelligent information systems. in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. 2000. 871~876
[58] S. DeLoach , M. Kokar. Category theory approach to fusion of wavelet based features. in: Procceeding of The 2rd Int. Conf. on Information Fusion. USA : Sunnyvale,1999. 610~617
[59] W. Ross , A. Waxman, et al. Multi Sensor 3D Image Fusion and Interactive Search.in: Proceedings of The 3rd Int. Conf. on Information Fusion. France: Paris. 2000. 501~508
[60] Rhodes, D. Luenberger. Differential games with imperfect state information. IEEE Transactions on Automatic Control, Feb 1969, 14(1): 29 ~38
[61] D. Dubois, H. Prade. Possibility Theory: An approach to the computerized processing of uncertain. New York: Plenum Press, 1988.
[62] P. Bosc, H. Prade. An introduction to fuzzy set and possibility theory based approaches to the treatment of uncertainty and imprecision in database management systems.in: Proceedings of The Sec. Workshop Uncertainty Management in Information Systems: from Needs to Solutions, Catalina,Calif.,1993.
[63] S. Post, P. Sage. An overview of automated reasoning. IEEE Transactions on Systems, Man, and Cybernetics, 1990, 20(1): 202~224
[64] S. Parsons. Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(3): 353~369
[65] M. F. Worboys, E. Clementini. Integration of imperfect spatial information. Journal of Visual languages and computing, 2001, 12:61~80
[66] Bordogna, S. Chiesa, D. Geneletti. Linguistic modeling of imperfect spatial information as a basis for simplifying spatial analysis. Information Science, 2006, 176:366~389.
[67] H. Wang, F. Smarandache, Y. Zhang and R. Sunderraman. Interval neutrosophic sets and logic: theory and applications in computing, Hexis, Arizona, 2005. Web available: http:// www.gallup.unm.edu/~smarandache /eBooks-otherformats.htm.
[68] J. Dezert, A. Tchamova, T.Semerdjiev, P. Konstantinova. Performance evaluation of fusion rules for multitarget tracking in clutter based on generalized data association .in: Procceeding of the 8th Int. Conf. on Information Fusion. USA: Philadelphia, July 2005. 2:1~8.
[69] R. Khedam, A. Bouakache, G. Mercier, A. Belhadj-Aissa. Improvement of Land Cover Map from Satellite Imagery using DST and DSmT. in: Procceeding of Information and Communication Technologies, 2006. April 2006. 1:383~388
[70] S. Bhattacharya, Utility, Rationality and Beyond - From Finance to Informational Finance. [ph.D dissertation], Bond University, Queensland, Australia, 2004.
[71] Elfes, H. Moravec. High resolution maps from wide angle sonar.in: Proceedings of IEEE Int Conf Robotics and Automation, 1985:116~121
[72] Elfes. Using occupancy grids for mobile robot perception and navigation. Computer, 1989, 22(6): 46~57
[73] Konolige. Improved occupancy grids for map building. Autonomous Robots, 1997, 4:351~367
[74] P. Arun, Tirumalai et al. Evidential reasoning for building environment maps. IEEE Transactions on System, Man, and Cybernetics,1995, 25(1): 10~20
[75] P. Daniel et al. An evidential approach to probabilistic map-building. in: Proceedings, IEEE Int. Conf. on Robotics and Automation. Minneapolis, Minnesota. April, 1996.745~750
[76]苏丽颖,曹志强,王硕等.多机器人对未知环境进行实时在线探测的一种方法.高技术通讯,2003,11:56~60
[77] R.HoseinNezhad, B.Moshirl, M. R. Asharif. Sensor fusion for ultrasonic and laser arrays in mobile robotics: A compareative study of fuzzy,Dsmpster and Bayesian approaches. in: Procceeding of Sensors 2002 by IEEE. Vol. 2, 12-14 June 2002. 1682 ~1689
[78]王卫华.未知环境中移动机器人创建地图研究.[博士论文].上海:上海交通大学图书馆,2003.
[79] J. J. Leonard, F. H. Durrant-White. Mobile robot localization by tracking geometric beacons. IEEE Transactions robotics and Automation. 1991, 7(30):376~382
[80] E. Wan, R. Merwe. The unscented kalman–filter for nonlinear estimations. in:Proceedings of IEEE Symposium (AS-SPCC). 2000.153~158
[81] W. Burgard, D. Fox, D. Hennig, T. Schmidt. Estimating the absolute position of a mobile robot using position probability grids.in: Procceeding of The National Conference on Artificial Intelligence (AAAI-1996). Oregon. 1996. 896~901.
[82] R. Ueda, T. Filase, Y. Kobayashi et al. Uniform Monte Carlo localization-fast and robust self-localization method for mobile robots.in: Proceedings of The 2002 IEEE International Conference on Robotics & Automation . Vol.5, 2002. 1353~1355
[83] J. S. Gutmann, W. Brugard, D. Fox. An experimental comparison of localization methods.in: Proceedings of IEEE International Conf on Intelligent Robots and Systems. Victoria, Canada. 1998. 736 ~743.
[84]房芳,马旭东,戴先中.基于霍夫空间模型匹配的移动机器人全局定位方法.机器人,2005, 27(1): 35~40
[85]尚文,马旭东,戴先中.融合多传感器信息的移动机器人自定位方法.东南大学学报(自然科学版) , 2004, 34(6): 784~788
[86] Zadeh, L. Fuzzy sets. Information and Control, 1965, 8(3):338~353
[87] D. Dubois and H. Prade. Théorie des possibilités: Applicationàla représentation des connaissances en informatique. Masson, Paris, 1985
[88] F. Smarandache.ed. Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, Gallup Campus, NM: Univ. of New Mexico, 1-3 December 2001
[89] Ashbacher. Introduction to neutrosophic logic. American Research Press, 2002.
[90] L Zadeh. On the validity of Dempster’s rule of combination. Memo M 79/24, Univ. of California, Berkeley, 1979.
[91] Zadeh. Review of mathematical theory of evidence. AI Magazine, 1984, 5(3): 81~83
[92] Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combination. Berkeley Cognitive Science Report No. 33, University of California, Berkeley, CA,1985
[93] L. Zadeh, A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine, 1986, 7(2): 85~90
[94] Dubois, H. Prade. On the unity of Dempster rule of combination, International Journal of Intelligent Systems, 1986, 1: 133~142
[95] F. Voorbraak. On the justification of Dempster’s rule of combination. Artificial Intelligence, 1991, 48:171~197
[96] J. Pearl. Reasoning with belief functions: an analysis of compatibility. International Journal of Approximate Reasoning, 1990, 4:363~389
[97] Dubois, H. Prade. A set-theoretic view of belief functions. International Journal of General Systems, 1986, 12:193~226
[98] Dubois, H. Prade, Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 1988, 4: 244~264
[99] P. Smets. Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate reasoning, 1993, 9: 1~35
[100] C. K. Murphy. Combining belief functions when evidence conflicts. Decision Support Systems, 2000, 29: 1~9
[101] R. R. Yager. On the relationships of methods of aggregation of evidence in expert systems. Cybernetics and Systems. 1985, 16: 1~21
[102] Klawonn, P. Smets. The dynamic of belief in the Transferable Belief Model and specialization generalization matrices. Uncertainty in Artificial Intelligence 92. Eds.D. Dubois, M. P. Wellman, B. D’Ambrosio, P. Smets, Morgan Kaufman, San Mateo, CA, 1992,130~137
[103] R. R. Yager. Hedging in the combination of evidence. Journal of Information and Optimization Science, 1983, 4(1): 73~81
[104] R. R. Yager. On the Dempster-Shafer framework and new combination rules. Information Sciences, 1987, 41: 93~138
[105] F. Smarandache. Unification of fusion theories (UFT). International Journal of Applied Mathematics & Statistics, 2004, 2: 1~14
[106] J. Dezert, F. Smarandache. On the generation of hyper-power sets for the DSmT.in: Proceedings of The 6th Int. Conf. Information Fusion. Australia: Cairns, Queensland.8-11, 2003. 1118-1125
[107] L. Comtet, Sperner Systems, sec.7.2 in Advanced Combinatories: The Art of Finiteand Infinite Expansions, D. Reidel Publ. Co.1974, 271~273
[108] R. Dedekind.über Zerlegungen von Zahlen durch ihre gr?ssten gemeinsammen Teiler, In Gesammelte Werke, Bd. 1. 1897, 103-148
[109] P. Smets. Data fusion in the transferable belief model. in: Proceedings of The 3rd International Conference on Information Fusion. Paris. July 10-13, 2000. PS21~PS33
[110] E. Lefevre, O. Colot, P. Vannoorenberghe. Belief functions combination and conflict management. Information Fusion Journal, 2002, 3(2):149~162
[111] L. Cheng, L. Kong, X. Li. Solving information fusion problems on unreliable evidential sources with generalized DSmT. Journal of Applied Sciences, 2006, 6(7): 1581~1585
[112] F. Smarandache(刘峰译).逻辑学的统一:中智逻辑中智学,中智集合论,中智概率论. Xiquan Publishing House, Chinese Branch, 2003. Web available: http://www.gallup.unm.edu/~smarandache/neutrosophy.htm
[113] T. Den?ux. Reasoning with Imprecise Belief Structures. International Journal of Approximate Reasoning, 1999, 20:79~111,
[114] T. Den?ux. Modeling Vague Beliefs Using Fuzzy-valued Belief Strictures. Fuzzy sets and Systems, 2000, 116:167-199
[115] F. Smaradache and J. Dezert. The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference. Web available: http://arxiv.org/PS_cache/cs/pdf /0412/0412091. pdf.
[116] T. Wagner, U. Visser, O. Herzog. Egocentric qualitative spatial knowledge representation for physical robots. Robotics and Autonomous Systems, 2004, 49(1-2): 25~42
[117] P. Ranganathan, J.B. Hayet, M. Devy et al. Topological navigation and qualitative localization for indoor environment using multi-sensory perception. Robotics and Autonomous Systems. 2002, 41,(2-3): 137~144
[118] Duckham, J. Lingham, K. Mason and M. Worboys. Qualitative reasoning about consistency in geographic information. Information Sciences, 2006, 176(6): 601~627
[119] Polya. Patterns of Plausible Inference. Princeton University Press, Princeton, NJ, 1954.
[120] L. Zadeh. A Theory of Approximate Reasoning. Machine Intelligence, 1979, 9:149~194
[121] L. Zadeh. Fuzzy logic = Computing with words. IEEE Transactions on Fuzzy Systems, 1996, 4(2): 103~111.
[122] M. P. Wellman. Some varieties of qualitative probability.in: Procceeding of The 5th Int. Conf. on Information Processing and the Management of Uncertainty (IPMU). Paris. July, 1994.
[123] S. Parsons and E. Mamdani. Qualitative Dempster - Shafer theory.in: Procceeding of the 3rd EMACS Int. Workshop on Qualitative Reasoning and Decision Technologies. Spain: Barcelona.1993.
[124] S. Parsons. Some qualitative approaches to applying Dempster-Shafer theory. Information and Decision Technologies, 1994, 19: 321~337
[125] S. Parsons. A proof theoretic approach to qualitative probabilistic reasoning. Int. J. of Approx. Reasoning, 1998, 19(3-4): 265~297
[126] Brewka, S. Benferhat and D.L. Berre. Qualitative choice logic, Artificial Intelligence, August 2004, 157(1-2): 203~237
[127] F. Smarandache, J. Dezert (Editors). Advances and Applications of DSmT for Information Fusion (Collected works). Vol.2, American Research Press, Rehoboth, 2006. Web available: http://www.gallup.unm.edu/ ?smarandache/DSmT-book2.pdf.
[128] B. Tessem.. Approximations for efficient computation in the theory of evidence. Artificial Intelligence, 1993, 61:315-329
[129] A. L. Jousselme, D. Grenier et al. A new distance between two bodies of evidence, Information fusion, 2001,2:91-101
[130] Diaz, M. Rifqi, B. Bouchon-Meunier, A Similarity Measure between Basic Belief Assignments.in: Procceeding of The 9th International Conference on Information Fusion. Italy: Florence. July 10-13, 2006.1~6
[131] B. Ristic. P. Smets. Association of Uncertain Combat ID Declarations. In: Procceeding of Cogis06 Conf.. Paris. March 2006.
[132] X. Li, J. Dezert, X. Huang. Selection of Sources as a prerequisite for Information Fusion with Application to SLAM.in: Procceeding, The 9th International Conference on Information Fusion. Italy: Florence. July 10-13, 2006.1~8
[133] Papoulis. Probability, Random Variables and Stochastic Processes. McGraw Hill, 4th Revised Edition, 2002.
[134] J. Dezert, Foundations for a new theory of plausible and paradoxical reasoning.International Journal Information and Security, 2002, 9:13-57
[135] Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distributions. IBull. Calcutta Math. Soc.,1943, 35: 99~109
[136] T. Kailath. The Divergence and Bhattacharyya Distance Measure in Signal Selection. IEEE Transactions on Communication Technology,1967 Com-15 1:52~60
[137] J. Dezert, F. Smarandache. Partial ordering of hyper-powersets and matrix representation of belief functions within DSmT.in: Proceedings of The 6th Int. Conf. on Information Fusion. Australia: Cairns, Queensland. 8-11 July 2003. 1230~2003
[138] J?sang, M. Daniel, P. Vannoorenberghe. Strategies for combing conflicting dogmatic beliefs.in: Proceedings of The 6th Int. Conf. on Information Fusion. Australia: Cairns, Queensland. Vol2, 8-11 July 2003. 1133~1140
[139] M. Daniel. Distribution of contradictive belief masses in combination of belief functions. Information, Uncertainty and Fusion, Chapter, Eds. B. Bouchon-Meunier, R.R.Yager, L.A Zadeh., Kluwer Academic Publishers, 2000, 431~446
[140] F. Smarandache, J. Dezert. Proportional conflict redistribution rules for information fusion. Applications and Advances of DSmT for Information Fusion (Collected works). Chapter, American Research Press, Rehoboth, Vol.2, 2006. Web available: http://www.gallup.unm.edu/?smarandache /DSmT-book2.pdf.
[141] F. Smarandache, J. Dezert. A Simple Proportional Conflict Redistribution Rule. International Journal of Applied Mathematics & Statistics,2005,3 (J05):1~36
[142] Martin, C. Osswald. A new generalization of the proportional conflict redistribution rule stable in terms of decision, Advances and Applications of DSmT for Information Fusion, Chapter, Amer. Res. Press, Rehoboth, 2006, http://www.gallup.unm.edu/ ~smarandache/DSmTbook2.pdf.
[143] G. Dissanayake, P. M. Newman et al. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robotics and Automation, 2001, 17(3): 229~241
[144] M. Montemerlo, S. Thrun. Simultaneous localization and mapping with unknown data association using Fast-SLAM. in: Proceedings of The IEEE int. conf. on Robotics and Automation. 2003.1985~1991.
[145] J. Dasvison, D. W. Murray. Simultaneous localization and mapping using active vision. IEEE Transactions on Pattern Analysis Machine Intelligence, 2002,24(7):865~880
[146] S. Thrun, D. Fox, W. Burgard et al. Robust monte carlo localization for mobile robots. Artificial Intelligence, 2001, 128: 99~141
[147] Castellanos, J.M.M. Montiel, J. Neira et al. Sensor influence. the performance of simultaneous mobile robot localization and map building. Eds. P. Corke and J. Trevelyan, editors, Experimental Robotics IV, Springer-Verlag, 2000: 287~296
[148] Elfes. Sonar-based real-world mapping and navigation. IEEE Journal of Robotics Automat, 1987, 3: 249~265
[149] Ofir Cohenand, Yael Edan. Adaptive fuzzy logic algorithm for grid-map based sensor fusion. in:Proceedings of 2004 IEEE Intelligent Vehicles Symposium. Italy. June 14-17 2004.625~630
[150] Baltzakis, P. Trahanias. An iterative approach for building feature maps in cyclic environments. in: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System.30 Sept.-5 Oct. Vol.1, 2002. 576 ~ 581
[151] G.A. Borges, M.J. Aldon. Optimal mobile robot pose estimation using geometrical maps.IEEE Transactions on Robotics and Automation, Feb. 2002,18(1):87~ 94
[152] Bandera, C. Urdiales, F. Sandoval. A hierarchical approach to grid-based and topological maps integration for autonomous indoor navigation. in: Proceedings of 2001 IEEE/RSJ International Conference onIntelligent Robots and Systems. Vol.2, 29 Oct.-3. Nov. 2001. 883 ~ 888.
[153] V. Egido, R. Barber, M.J.L Boada et al. Self-generation by a mobile robot of topological maps of corridors. in: Proceedings of IEEE International Conference on Robotics and Automation. Vol.3, 11-15 May 2002. 2662 ~ 2667
[154] E. Rybski, F.Zacharias, J. F Lett. Using visual features to build topological maps of indoor environments. in: Proceedings of IEEE International Conference on Robotics and Automation. Vol.1, 14-19 Sept. 2003. 850 ~ 855
[155]庄严,徐晓东,王伟.移动机器人几何一拓扑混合地图的构建及自定位研究.控制与决策, 2005, 20(7):815~822。
[156] Jensfelt,H. I. Christensen.Pose Tracking Using Laser Scanning and Minimalistic Environmental Models. IEEE Trans on Robotics and Automation,2001,17(2):138~147.
[157] N. Tomatis, I. Nourbakhsh, R. Siegwart. Hybrid Simultaneous Localization andMap Building:A Natural Integration of Topological and Metric.Robotics and Autonomous Systems,2003,44(1):3~14.
[158] X. Li, X. Huang, J. Dezert, et al. A successful application of DSmT in sonar grid map building and comparison with DST-based approach.International Journal of Innovative Computing, Information and Control, 2007,3(3)
[159] X. Li, X. Huang, M. Wang. Sonar grid map building of mobile robots based on DSmT. Information Technology Journal, 2006, 5(2):267~272
[160]李新德,黄心汉,王敏.基于信度赋值技术的统一融合框架(UFA)研究.华中科技大学学报, 2007,35 (01):46~49
[161]李新德,黄心汉, Jean Dezert.基于ESMS过滤器的信息融合理论研究及SLAM应用.计算机科学, 2006,33(12):117~122
[162]叶涛等.全局环境未知时机器人导航和避障的一种新方法.机器人,2003, 25(6) :516~520
[163] Cai, t. Fukuda et al. Integration of distributed sensing information in DARS based on evidential reasoning. in:Proceedings of The 3rd International Symposium on Distributed Autonomous Robotic Systems.1996.268~279
[164] S. Thrun, D. Fox, W.Burgard. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning, 1998, 31(1-3) :29~53
[165] C.F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions On Robotics and Automation, 2000, 16(1):55~66
[166] Saffiotti. The use of fuzzy logic in autonomous robot navigation: a catalogue raisonne. http://irida.ulb.ac.be/FLAR/survey.html
[167]王卫华,陈卫东,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人, 2001, 23( 6): 563~568
[168] X. Li, X. Huang, M. Wang. Robot map building from sonar sensors and DSmT. INFORMATION & SECURITY. An International Journal, 2006, 20: 104~121
[169] D. Fox. Adapting the sample size in particle filters through KLD-Sampling. International Journal of Robotic Research, 2003, 22(2):985-1004.
[170]贾志刚.精通OpenGL.(第1版).北京:电子工业出版社, 1998年08月
[171]乔林费广正等. OpenGL程序设计. (第1版).北京:清华大学出版社, 2000年4月