机载多传感器目标信号属性融合研究
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摘要
随着战场环境日益复杂恶劣,为提高飞机武器系统的作战性能,研究复杂干扰环境下机载多传感器目标识别问题具有极其重要的意义。为此,本文研究了复杂干扰条件下的机载多传感器属性融合问题,主要取得的成果为:
1)本文首先分析了复杂干扰环境下属性融合存在融合信息不确定性大,造成系统鲁棒性和自适应性变差的问题,提出一种自适应融合模型。模型分三个部分:不确定信息处理、分类识别信息融合、知识更新。其中知识更新模块利用智能技术解决不同环境和信息层次下融合系统所要求的环境自适应性,稳健性问题。在此基础上,本文研究了知识更新模块的建立,提出利用蚁群算法进行知识的智能分析和推理寻优,从而实现在复杂环境下知识和更新的自适应管理。为进一步提高算法性能,本文研究了蚁群算法的改进问题,提出了爬山变异蚁群算法,将蚂蚁算法和局部搜索算法结合起来,提高算法后期的计算效率。
2)针对数据层属性融合问题,本文分别研究了T.Fukuda和Luo R.C提出的不同算法,在此基础上,本文提出了一种基于传感器信息的一致性和可信度的分组融合算法。算法通过修正类别置信度的分类估计,减小测量误差引起的目标分类置信度的估计偏差;通过一致性测度进行传感器分组,利用传感器先验可信度估计各分组传感器的可信度,再优选可信度高的传感器组进行融合,从而修正冲突信息对融合结果的影响。该算法将不确定信息处理和融合结构有机结合,提高了属性融合的抗干扰能力。
3)针对硬决策属性融合问题,本文首先研究了目标先验知识已知的情况下,分布式检测系统的算法优化算法,如穷举搜索算法、混合搜索算法,发现这些算法大多是次优算法,所以本文提出了基于爬山变异蚁群算法的贝叶斯全局优化算法。然后针对复杂环境及目标先验知识未知情况,研究了主观BAYES算法、NP算法,发现这些算法的性能和自适应性很难同时提高,所以本文提出了基于一致性分组和分类学习策略和爬山变异蚁群算法的贝叶斯全局优化算法,该算法具有全局优化能力和环境的自适应性。
4)针对软决策属性融合问题,本文在深入研究模糊积分的基础上,改进传感器的模糊积分密度的定义,提出一种采用爬山变异蚁群算法确定统计模糊积分密度,采用环境模糊积分密度修正环境干扰的学习算法,并对比了采用遗传算法、神经网络等不同算法的性能,发现本文算法能明显提高算法的环境自适应性和在复杂环境下的融合性能较优。
5)最后,本文研究了在不同的机动条件及电磁干扰条件下,上述算法在机载平台战场环境下的目标识别和机载流量传感器故障识别中的应用,验证了算法的有效性。
With the battlefield getting increasingly complex, it is significant to study airborne multi-sensor target identification in a complex interference environment in order to improve performance of aircraft weapon system. So, in this paper, the attribute fusion problem of airborne multi-sensor is mainly studied. The results are as following:
1) Firstly, based on an analysis of the problems of attribute fusion in a complex interference environment, such as uncertainty of fusion information to make the system's robustness and self-adaptation worse, a self-adaptive fusion model is brought forward. The model is constituted by three parts: uncertain information processing, classified information fusing and knowledge updating. The knowledge updating utilizes intelligence technology to meet the requirements of environmental self-adaptation and robustness of data fusion system working in a changeful environment and information level. Based on that model, this paper studies foundation of information-updating module and then prensents knowledge's intellectualized analysis and consequence optimization by ant colony algorithm to realize self-adaptation management of knowledge and updating in a complex environment. To improving algorithm, the paper researches amelioration problem of ant colony algorithm, and then presents Ant System with Bit Climbing Aberrance, which combine ant colony algorithm with part-search to improve arithmetic's evening calculative efficiency.
2) In view of attribute fusion problems in data level, this paper researches different algorithm presented by T.Fukuda and Luo R.C and then presents grouping fusion algorithm based on consistency and reliability of sensors' information. This algorithm amends classification estimation of the classification confidence to reduce the estimation error of object's classification confidence made by measure error; classes sensors by consistency measure and estimates the reliability of different sensor groups by the sensors' transcendent reliability; lastly, chooses the best sensor group of reliability to fuse, which can amend influence of conflict information. This algorithm combines uncertain information processing with the fusion structure organically, which enhances anti-interference capability of attribute fusion.
3) In view of attribute fusion problem of hard-decision, this paper studies algorithm optimization of the distributed detection system when object transcendent information was known, such as searching algorithm and mixed searching algorithm. After discovering that these algorithms are inferior-optimizing, this paper presents Bayesian global-optimization algorithm based on Ant System with Bit Climbing Aberrance. For the complex environment and unknown background knowledge about targets, this paper discovers that subjective BAYES and NP algorithm is difficultly to improve the performance and self-adaptation at the same time, and then brings forward BAYES global-optimization algorithm based on consistency grouping, strategy of classified learning and Ant System with Bit Climbing Aberrance. This algorithm has self-adaptation of environment and is global-optimizing.
4) In view of attribute fusion problem of soft-decision, on the base of fuzzy integral, this paper gives a new definition about density of fuzzy integral, presents an algorithm which makes use of Ant System with Bit Climbing Aberrance to ascertain Statistic integral density, uses Environment integral density to amend environment molestation and discovers that this algorithm can enhance self-adaptation of environment and performance of this algorithm is better, after contrasting performance of genetic algorithm and NN algorithm.
5) Lastly, this paper applies the above algorithm to target identification in airborne battlefield environment and failure diagnosis of airborne flux- temperature sensors in the different condition of aviation and electromagnetism interference, which validates its effectiveness.
1)本文首先分析了复杂干扰环境下属性融合存在融合信息不确定性大,造成系统鲁棒性和自适应性变差的问题,提出一种自适应融合模型。模型分三个部分:不确定信息处理、分类识别信息融合、知识更新。其中知识更新模块利用智能技术解决不同环境和信息层次下融合系统所要求的环境自适应性,稳健性问题。在此基础上,本文研究了知识更新模块的建立,提出利用蚁群算法进行知识的智能分析和推理寻优,从而实现在复杂环境下知识和更新的自适应管理。为进一步提高算法性能,本文研究了蚁群算法的改进问题,提出了爬山变异蚁群算法,将蚂蚁算法和局部搜索算法结合起来,提高算法后期的计算效率。
2)针对数据层属性融合问题,本文分别研究了T.Fukuda和Luo R.C提出的不同算法,在此基础上,本文提出了一种基于传感器信息的一致性和可信度的分组融合算法。算法通过修正类别置信度的分类估计,减小测量误差引起的目标分类置信度的估计偏差;通过一致性测度进行传感器分组,利用传感器先验可信度估计各分组传感器的可信度,再优选可信度高的传感器组进行融合,从而修正冲突信息对融合结果的影响。该算法将不确定信息处理和融合结构有机结合,提高了属性融合的抗干扰能力。
3)针对硬决策属性融合问题,本文首先研究了目标先验知识已知的情况下,分布式检测系统的算法优化算法,如穷举搜索算法、混合搜索算法,发现这些算法大多是次优算法,所以本文提出了基于爬山变异蚁群算法的贝叶斯全局优化算法。然后针对复杂环境及目标先验知识未知情况,研究了主观BAYES算法、NP算法,发现这些算法的性能和自适应性很难同时提高,所以本文提出了基于一致性分组和分类学习策略和爬山变异蚁群算法的贝叶斯全局优化算法,该算法具有全局优化能力和环境的自适应性。
4)针对软决策属性融合问题,本文在深入研究模糊积分的基础上,改进传感器的模糊积分密度的定义,提出一种采用爬山变异蚁群算法确定统计模糊积分密度,采用环境模糊积分密度修正环境干扰的学习算法,并对比了采用遗传算法、神经网络等不同算法的性能,发现本文算法能明显提高算法的环境自适应性和在复杂环境下的融合性能较优。
5)最后,本文研究了在不同的机动条件及电磁干扰条件下,上述算法在机载平台战场环境下的目标识别和机载流量传感器故障识别中的应用,验证了算法的有效性。
With the battlefield getting increasingly complex, it is significant to study airborne multi-sensor target identification in a complex interference environment in order to improve performance of aircraft weapon system. So, in this paper, the attribute fusion problem of airborne multi-sensor is mainly studied. The results are as following:
1) Firstly, based on an analysis of the problems of attribute fusion in a complex interference environment, such as uncertainty of fusion information to make the system's robustness and self-adaptation worse, a self-adaptive fusion model is brought forward. The model is constituted by three parts: uncertain information processing, classified information fusing and knowledge updating. The knowledge updating utilizes intelligence technology to meet the requirements of environmental self-adaptation and robustness of data fusion system working in a changeful environment and information level. Based on that model, this paper studies foundation of information-updating module and then prensents knowledge's intellectualized analysis and consequence optimization by ant colony algorithm to realize self-adaptation management of knowledge and updating in a complex environment. To improving algorithm, the paper researches amelioration problem of ant colony algorithm, and then presents Ant System with Bit Climbing Aberrance, which combine ant colony algorithm with part-search to improve arithmetic's evening calculative efficiency.
2) In view of attribute fusion problems in data level, this paper researches different algorithm presented by T.Fukuda and Luo R.C and then presents grouping fusion algorithm based on consistency and reliability of sensors' information. This algorithm amends classification estimation of the classification confidence to reduce the estimation error of object's classification confidence made by measure error; classes sensors by consistency measure and estimates the reliability of different sensor groups by the sensors' transcendent reliability; lastly, chooses the best sensor group of reliability to fuse, which can amend influence of conflict information. This algorithm combines uncertain information processing with the fusion structure organically, which enhances anti-interference capability of attribute fusion.
3) In view of attribute fusion problem of hard-decision, this paper studies algorithm optimization of the distributed detection system when object transcendent information was known, such as searching algorithm and mixed searching algorithm. After discovering that these algorithms are inferior-optimizing, this paper presents Bayesian global-optimization algorithm based on Ant System with Bit Climbing Aberrance. For the complex environment and unknown background knowledge about targets, this paper discovers that subjective BAYES and NP algorithm is difficultly to improve the performance and self-adaptation at the same time, and then brings forward BAYES global-optimization algorithm based on consistency grouping, strategy of classified learning and Ant System with Bit Climbing Aberrance. This algorithm has self-adaptation of environment and is global-optimizing.
4) In view of attribute fusion problem of soft-decision, on the base of fuzzy integral, this paper gives a new definition about density of fuzzy integral, presents an algorithm which makes use of Ant System with Bit Climbing Aberrance to ascertain Statistic integral density, uses Environment integral density to amend environment molestation and discovers that this algorithm can enhance self-adaptation of environment and performance of this algorithm is better, after contrasting performance of genetic algorithm and NN algorithm.
5) Lastly, this paper applies the above algorithm to target identification in airborne battlefield environment and failure diagnosis of airborne flux- temperature sensors in the different condition of aviation and electromagnetism interference, which validates its effectiveness.
引文
[1]杨静宇,邬永革等.战场数据融合技术.北京,兵器工业出版社.1994:3-5
[2]刘同明,夏祖勋等.数据融合技术及应用.北京,国防工业出版社.1998:2-11
[3]Hall D.L.Mathemeticial techniques in multisensor data fusion.Artech Housem.1992:22-25
[4]Singer R.A.,Sea R.G.New result in optimizing surveillance system tracking and data correlation performance in dense multi-target environments.IEEE transactions on Automatic Control.1973:571-582
[5]Wohl J.G Force management requirements for air force tactical command and control.IEEE transactions on System,man and cybernetics.1981:618-639
[6]Okello N.N,Thomopoulos S.C.A.Design of Data Fusion System for Mulfiradar Target Detection.IEEE transactions on Systems,Man,and Cybernetics.France,Oct 1993:672-677
[7]Yager R.R.A framework for multi-source data fusion.Information Sciences,2004(163):175-200
[8]Yager R.R.Intelligent decision making and information fusion,IEEE International Conference:intelligent systems.June 2004:1-4
[9]Thomopoulos S.C.A.Sensor Selectivity and Intelligent Data Fusion.IEEE International Conference:Multisensor Fusion and Integration for Intellgent Systems.Oct.1994:529-537
[10]Gonsalves P.G.,Rinkus G.J.Intelligent Fusion and Asset Management Processo.IEEE Information Technology Conference:Information Environment for the Future.1998:15-18
[11]Ren C.Luo,Chih Chen Yih,Kuo Lan Su.Multisensor Fusion and Integration:Approaches,Applications,and Future Research Directions.IEEE Sensors Jounal.2002,2(2):107-110
[12]Dasarathy B.V.Intelligent Learning Techniques for Multi-source Information Fusion Environments.Proceedings of the 37th IEEE Conference on Decision & Control Tampa.Dec.1998:21-22
[13]Francois Delmote,Pierre Borne.Modeling of Reliability with Possibility Theory.IEEE transactions on System,Man,and Cybernetics,Part A:Systema and humans.1998,28(1):78-88
[14]Nikunja K.Swain,James A.Anderson,A.M.Hasanul Basher.Intelligent Sensor Integrated System.Part of the SPIE Conference on Sinai Processing,Sensor Fusion,and Target Recognition Ⅶ.Orlando Florida.April 1998:347-351
[15]Yager R.R.Approximate leasoning and confict Resolution.International Journal of Approximate Reasoning.2000(25):15-42
[16]Kobayashi F.Masumoto D,Kojima E Sensor Selection based Sensor on Fuzzy Inference for sensor Fusion.2004 IEEE International Conference on Fuzzy systems.2004(1):305-310
[17]Manish Kumar,Devendra Garg Randy Zachery.Stochastic adaptive sensor modeling and data fusion,Smart Structures and Materials 2006 Proc.Of SPIE Vol.6174
[18]敬忠良,杨永胜等.基于模糊神经网络和D-S推理的智能特征信息融合研究.信息与控制,1997(26):107-111
[19]王婷杰,施慧昌.一种基于模糊理论的一致性数据融合方法.传感器技术.1999,18(6):50-54
[20]童树鸿,沈毅等.数据融合的模糊模型和算法研究.哈尔滨工业大学学报.2002,34(1):7-10
[21]孙庆伟.复杂干扰环境下多传感器数据融合方法及应用.哈尔滨工业大学博士学位论文.2002:13-15,26-35
[22]权太范.信息融合神经网络-模糊推理理论与应用.北京:国防工业出版社,2003·309-321
[23]Yan Huaizhi,Hu Changzhen,Huimin.Multi-sensor information intelligence fusion model using fuzzy colored Petri nets.Proceedings of the 5th World Congress on Intelligent Control and Automation.Hangzhou,ER.China,June 2004:3072-3075
[24]Quan Liu,Xiao mei zhang.An adaptive multisensor data fusion system basede on wavelet denoising neural network,Sensors for Havsh Environments II,Proc.Of SPIE Vol.5998,2005
[25]Pooyan Khajehpour Tadavani,Behzad Moshiri.Adaptive Data Fusion Struture.CIMCA-IAWTIC'06,0-7695-7231-0106
[26]文成林,潘泉,张洪才,戴冠中.多传感器单模型动态系统多尺度分布式融合估计.控制理论与应用.2002.12:841-846
[27]文成林,吕冰,葛泉波.一种基于分布式滤波的数据融合算法,电子学报.2004.08:1264-1267
[28]马忠孝,刘宗玉,陈明.基于模糊逻辑的自适应卡尔曼滤波在GPS/INS组合导航中的应用.信息与控制.2006.08:457-461
[29]王天荆,杨震,胡海峰.基于遗传算法的无线传感器网络自适应数据融合路由算法.电子与信息学报.2007.09:2244-2247
[30]李芳芳,宋晋敏.自适应数据融合算法.现代防御技术.2001.10:50-54
[31]Hong Luo,Jan Luo,Yonghe Liu.Adaptve Data Fusion for Energy Efficient Routiong in wireless Sensor Networks.IEEE transactions on computers.Vol.55,No.10 2006:1286-1299
[32]丁兴俊,周德云,胡昌华,王青.基于主被动雷达双传感器的自适应跟踪融合算法.弹箭与制导学报.2006.01:147-149
[33]Leonid A.Fokin.Multisensor Data Fusion in Adaptive Astro-Satellite-Inertial Navigation System.Siberian Confence on Control and Communications.2007:22-28
[34]黄鹤.机载多传感器数据融合的数据链应用技术.电讯技术,2004,(4):103-106
[35]孙红岩,毛士艺,林品兴.多传感器数据分层融合的性质.电子学报,1996,24(6):55-61
[36]王军,苏剑波,席裕庚.传感器融合综述.数据采集与处理,2004,19(1):72-77
[37]M.Dorigo,G.D.Lavo.Ant Algorithms for Discrete Opetimization.Artificial Life,1999,5(3):137-172
[38]Holland,J.H.Outline for a logical theory of adaptive systems.Journal of the Association for Computing Machinery,1962,9(3):297-314
[39]陈国良.并行计算一结构、算法、编程.北京:高等教育出版社,2001,62-71
[40]李士勇.蚁群算法及其应用.哈尔滨,哈尔滨工业大学出版社,2004,53-62
[41]http://www.iwr.uni-heidelberg.de/iwr/comopt/software/TSPLIB95
[42]M Dorigo,V Maniezzo,A colomi.The Ant system:Optimization by a Colony of Cooperationg Agents.IEEE Trans.On Systems Man Cybemet,1996,(B26):29-41
[43]吴斌,史忠植.一种基于蚁群算法TSP问题分段求解算法.计算机学报.2001,24(12):1328-1333
[44]R.Schoonderwoerd,O.Holland,J.Bruten.Ant-based Load Balancing in Telecommunications Networks.Adaptive Behavior.1997,5(2):169-207
[45]燕忠,袁春伟.用蚁群优化算法求解中国旅行商问题.电路与系统学报.2004,9(3):122-126
[46]L.Ma,F.Jiang.Ant Algorithm for Degree-constrained Minimum Spanning Tree.Journal of Systems Engineering.1999,14(3):211-214
[47]T.Stutzle,H.H.Hoos.MAX-MIN Ant System.Future Generation Computer Systems.2000,16(8):889-914
[48]J.H.Zhang,Q.S.Gao,X.H.Xu.A Self-adaptive Ant Colony Algorithm.Control Theory and Applications.2000,17(1):1-3
[49]李敏强、寇纪凇等,遗传算法的基本理论与应用.北京,科学出版社,2002.03:224-227
[50]T.Fukuda,K.Shimojima,E Arai,H.Matsuura.Multi-sensor integration system utilizing fuzzy inference and neural network.Information and Science,1992,71(1):27-41
[51]K.Shimojima,T.Fukuda,F.Arai,H.Matsuura.Fuzzy inference integrated 3-D measuring System with LED Displacement sensor and Vision System.Intelligent and Fuzzy Systems,1993,1(1):63-72
[52]Luo R.C.Dynamic Multisensor Data Fusion System for Intelligent Robots.IEEE Journal of Robotics and Automation.1988,4(4):386-396
[53]刘敏华,萧德云.基于相似度的多传感器数据融合.控制与决策.2004,9(5):534-537
[54]Luo R.C.,Min-Hsiung Lin,Ralph S.Scherp.The issues and approaches of arobot multisensor integration.IEEE International Conference on Robotics and Automation.1987,(3):1941-1946
[55]涂国平,邓群钊.多传感器数据的统计融合方法.传感器技术.2001,20(3):28-29,32
[56]卢大伟.多传感器数据融合方法在新型组合航姿系统中的应用.东南大学博士论文.2001.12:69-79
[57]蒲书缙,赵忠凯.基于一致理论的多源信息融合研究.应用科技.2006,33(2):13-15
[58]V.V.S.Sarma,Savithri Raju.Multisenor Data Fusion and Decision Support for Airbome Target identification.IEEE Transaction on Systems,Man,and Cybernetics.1991,21(5):1224-1230
[59]Thomas W.Automata on infinite objects.In:Lee wen JV ed.Handbook of Theoretical Computer Science.North-Holland:Else-vier Science Publisher.1990,(1):54-59
[60]Tenney R.R.,Sandell N.R.Detection with Distributed Sensors.IEEE Transactions on Aerospace and Electronic Systems,July 1981,17(4):561-510
[61]V.V.S.Sarma,Rao K.A.Decentralized Detection and Estimation in Distributed Sensor Systems.In Proceedings of the IEEE Cybernetics and Society Confenrence.1983,438-441
[62]Lauer G.S.,Sandell N.R.Distributed Detection with Waveform Observations:Correlated Observation Processes.Proceedings of the American Controls Conference.1981:812-819
[63]Sadjadi EA.Hypotheses testing in a Distributed Envrinments.IEEE Transactions on Aerospace and Electronic Systems.1986,22(1):134-137
[64]Chair Z.,Varshney P.K.Optimal Data Fusion in Multiple Sensor Detection Systems.IEEE Trans.on Aerospace and Electronic Systems.1986,22(A):98-101
[65]V.Venugopal,V.Veeravalli,T.Basar,H.V.Poor.Decentralized Sequential Detection with a Fusion Center Performing the Sequential Test.IEEE Transaction on Information Theory.1993,27:433-442
[66]V.Veeravalli,T.Basar,H.V.Poor.Mini max Robust Decentralized Detection.IEEE Transaction on Information Theory.1994,(38):35-40
[67]Kang Z.B.,K.R.Pattipati,D.K.K1einman.Optimization of Distributed Detection Networks.IEEE Transtaction on System,Man and Cybernatics.1993,23:211-221
[68]李宏.分布式多传感器数据融合理论、算法与应用.西北工业大学博士论文.2000.08:19-21
[69]Chen P.,A.Papamarcou.Error Bounds for Parallel distributed Detection under the Neyman -Pearson Criterion.IEEE Trans.on Info.Theory,March 1995,41:528-533
[70]Willet P.,Warren D.,Decentralized Detection:When are identical sensors identical.Proceedings of Conf.on Inform Sci.and Syst.,1991:287-292
[71]Tsitsiklis J.N.Decentralized Detection with A Large Number of Sensors.Mathematic Contr.And Signals Syst.1988,38:167-182
[72]Hashlamoun W.A.,Varshney EK.An Approach to Design of Distributed Bayesian Detection Structures.IEEE Trans.on Syst.,Man,and Cybernetics.1991,21(5):1206-1211,
[73]Thomopoulos SCA.,Viswanathan R.Globally optimal computable distributed detection fusion.The 26st IEEE Conference on Decoction and Control.1987:1846-1847
[74]Chair Z.,Ek.Varshney.Neyman-Pearson Hypothesis Testing in Distributed Networks.Proc.26th IEEE Trans.on Decision.and Control.1987:1842-1843
[75]Drakopoulos E.,C.C.Lee.Optimum Multisensor Fusion of Correlated Local Decisions.IEEE Trans.on Aerospace and Elect.Syst.1991,27(4):593-605
[76]Blum R.S.Necessary Conditions for Optimum Distributed Sensor Detectors under the Neyman-Pearson Criterion.IEEE Trans.on Info.Theory.1996,42(3):990-994
[77]Willett P.,D.Warren.The Suboptimality of Randomized Tests in Distributed and Quantized Detection Systems.IEEE Trans.on Info.Theory,March 1992,38(2):355-361
[78]孟伟,赵健等.基于神经网络与主观Bayes理论的雷达识别研究.计算机工程,2002,28(7):24-27
[79]孙红岩,张钱等.主观Bayes方法与神经网络相结合的多传感器数据融合空中点目标识别方法.红外与毫米波学报,1997,16(6):448-454
[80]涂国平,邓群钊.多传感器数据的统计融合方法.传感器技术,2001.20(3):28-29,32
[81]S.C.A.Thomopoulos,R.Viswanathan,D.Bougoulias.Optimal Decision Fusion in Multiple Sensor Systems.IEEE Trans.on AES,September.1987,AES-23(5):644-653
[82]D.M.Buede,E.L.Waltz.Benefits of Soft Sensors and Prohabilistic Fusion.SPIE Signal and Data Processing of Small Targets.1989,1096:309-320
[83]R.Krzysztofowicz,D.Long.Fusion of Detection Probabilities and Comparison of Multisensor Systems.IEEE Trans SMC.1990,20(3):665-677
[84]T.M.Strat.Continuous Belief Functions for Evidential Reasoning.Porcessing National Conference on Artificial Intelligence.1984:308-313
[85]L.A.Zadeh.Review of Books:A Mathematical Theory of Evidence.The AI Magazine.1984,5(3):81-83
[86]D.Dubois,H.Prude.Combination and Propagation of Uncertainty with Belief Functions-A Reexamination.Proceedings 9th International Joint Conference on Artificial Intelligence.1985:111-113
[87]R.M.Yannone,P.P.Bonissone.Attribute Fusion and Situation Assessment with a Many-Valued Logic Approach.Artech House INC.1992,2,Chapter1 1,
[88]D.Dubois,H.Prade.Criteria Aggregation and Ranking of Altemativesin the Framework of Fuzzy Set Theory.New York,Elsevier Science Publishers.1984,20:209-240
[89]J.H.Zhang,Q.S.Gao,X.H.Xu.A Self-adaptive Ant Colony Algorithm.Control Theory and Applications.2000,17(1):1-3
[90]Ralescu D.A,Adams G.The fuzzy integral.J.Math.Annual.1980,75:562-570
[91]赵汝怀.(N)模糊积分.数学研究与评论,1981,2:55-72
[92]张文修,赵汝怀.模糊测度与模糊积分的推广.模糊数学,1983,3:1-8
[93]Gareia F.S.,Alvarez P.G.Two families of fuzzy integrals.Fuzzy Sets and Systems.1986,18:67-81
[94]Garcia F.S.,Alvarez P.G.Measures of fuzziness of fuzzy events.Fuzzy Sets and Systems.1987,21:147-157
[95]Suzuki H.On fuzzy measures defined by fuzzy integrals.Math.Annual.1988,132:87-101
[96]张德利,郭彩梅,王中海.Fuzzy测度序列的收敛性和积分序列的收敛定理.东北师范大学学报(自).1993,4:11-14
[97]Ha M.H.,Wang X.Z.Weak convergence of fuzzy measure sequences.Fuzzy Math.1995,3:383-391
[98]Greco G.,Bassanezi R.C.On the continuity of fuzzy integrals.Fuzzy Sets and Systems.1993,53:87-91
[99]Flores H.R.,Franulic A.E On the level continuity of fuzzy integrals.Fuzzy Sets and Systems.1996,80:339-344
[100]Ralescu D.A.,Sugeno M.Fuzzy integral representation.Fuzzy Sets and Systems.1996,84: 127-133
[101]Squillante M.,Ventre A.Representations of the fuzzy integral.Fuzzy Sets and Systems,1989,29:165-169
[102]王子孝.模糊积分与模糊性度量.模糊数学,1982,1:57-68
[103]张广全.乘积空间上的SC-Fuzzy测度与Fuzzy积分.河北大学学报.1988,2:1-11
[104]何家儒.S型Fuzzy乘积测度与重积分.四川师范大学学报(自).1987,1:1-10
[105]Mesiar R.,Sipos J.Radon Nikodym like theorem for fuzzy measures.Fuzzy Math.1993,1:873-878
[106]Sugeno M.,Murofushi T.Pseudo-additive measures and integrals.Math.Annual.1987,122:197-222
[107]Benediktsson J.A.,Swain P.H.Consensus Theoretic Classification Methods.IEEE Trans.System an Cybemet.1992,(22):688-704
[108]Benediktsson J.A.,Sveinsson J.R.Optimied Consensus Theoretic.IEEE Trans.Geosci.Remote Sensing.1996:3490-3493
[109]K.C.Chang,R.K.Saha,Y.Bar-Shalom.On Optimal Track-to-Track Fusion.IEEE Trans.On AES.1997,33(4):1271-1276
[110]Zhou B.,Bose N.K.A comprehensive analysis of neural solution to the multi target tracking data association problem.IEEE Trans.AES.1993,29(6):260-263
[111]丁赤飚,毛士艺.基于雷达和成像传感器的融合跟踪.电子学报.1998,26(9):134-138
[112]G.J.Klir,B.Yuan,J.F.Swan-Stone.Constructing fuzzy measures from given data.Proc.6th IFSA Congress.1995:61-64
[113]J.Wang.Determining fuzzy measures by using statistics and neural networks.Proc.6th IFSA Congress.1995:519-521
[114]Keno Mynry Lee,Hyung Lee Kwang.Identification of A fuzzy measure by genetic algorithm s.Fuzzy Sets and Systems.1995,75(3):301-309
[2]刘同明,夏祖勋等.数据融合技术及应用.北京,国防工业出版社.1998:2-11
[3]Hall D.L.Mathemeticial techniques in multisensor data fusion.Artech Housem.1992:22-25
[4]Singer R.A.,Sea R.G.New result in optimizing surveillance system tracking and data correlation performance in dense multi-target environments.IEEE transactions on Automatic Control.1973:571-582
[5]Wohl J.G Force management requirements for air force tactical command and control.IEEE transactions on System,man and cybernetics.1981:618-639
[6]Okello N.N,Thomopoulos S.C.A.Design of Data Fusion System for Mulfiradar Target Detection.IEEE transactions on Systems,Man,and Cybernetics.France,Oct 1993:672-677
[7]Yager R.R.A framework for multi-source data fusion.Information Sciences,2004(163):175-200
[8]Yager R.R.Intelligent decision making and information fusion,IEEE International Conference:intelligent systems.June 2004:1-4
[9]Thomopoulos S.C.A.Sensor Selectivity and Intelligent Data Fusion.IEEE International Conference:Multisensor Fusion and Integration for Intellgent Systems.Oct.1994:529-537
[10]Gonsalves P.G.,Rinkus G.J.Intelligent Fusion and Asset Management Processo.IEEE Information Technology Conference:Information Environment for the Future.1998:15-18
[11]Ren C.Luo,Chih Chen Yih,Kuo Lan Su.Multisensor Fusion and Integration:Approaches,Applications,and Future Research Directions.IEEE Sensors Jounal.2002,2(2):107-110
[12]Dasarathy B.V.Intelligent Learning Techniques for Multi-source Information Fusion Environments.Proceedings of the 37th IEEE Conference on Decision & Control Tampa.Dec.1998:21-22
[13]Francois Delmote,Pierre Borne.Modeling of Reliability with Possibility Theory.IEEE transactions on System,Man,and Cybernetics,Part A:Systema and humans.1998,28(1):78-88
[14]Nikunja K.Swain,James A.Anderson,A.M.Hasanul Basher.Intelligent Sensor Integrated System.Part of the SPIE Conference on Sinai Processing,Sensor Fusion,and Target Recognition Ⅶ.Orlando Florida.April 1998:347-351
[15]Yager R.R.Approximate leasoning and confict Resolution.International Journal of Approximate Reasoning.2000(25):15-42
[16]Kobayashi F.Masumoto D,Kojima E Sensor Selection based Sensor on Fuzzy Inference for sensor Fusion.2004 IEEE International Conference on Fuzzy systems.2004(1):305-310
[17]Manish Kumar,Devendra Garg Randy Zachery.Stochastic adaptive sensor modeling and data fusion,Smart Structures and Materials 2006 Proc.Of SPIE Vol.6174
[18]敬忠良,杨永胜等.基于模糊神经网络和D-S推理的智能特征信息融合研究.信息与控制,1997(26):107-111
[19]王婷杰,施慧昌.一种基于模糊理论的一致性数据融合方法.传感器技术.1999,18(6):50-54
[20]童树鸿,沈毅等.数据融合的模糊模型和算法研究.哈尔滨工业大学学报.2002,34(1):7-10
[21]孙庆伟.复杂干扰环境下多传感器数据融合方法及应用.哈尔滨工业大学博士学位论文.2002:13-15,26-35
[22]权太范.信息融合神经网络-模糊推理理论与应用.北京:国防工业出版社,2003·309-321
[23]Yan Huaizhi,Hu Changzhen,Huimin.Multi-sensor information intelligence fusion model using fuzzy colored Petri nets.Proceedings of the 5th World Congress on Intelligent Control and Automation.Hangzhou,ER.China,June 2004:3072-3075
[24]Quan Liu,Xiao mei zhang.An adaptive multisensor data fusion system basede on wavelet denoising neural network,Sensors for Havsh Environments II,Proc.Of SPIE Vol.5998,2005
[25]Pooyan Khajehpour Tadavani,Behzad Moshiri.Adaptive Data Fusion Struture.CIMCA-IAWTIC'06,0-7695-7231-0106
[26]文成林,潘泉,张洪才,戴冠中.多传感器单模型动态系统多尺度分布式融合估计.控制理论与应用.2002.12:841-846
[27]文成林,吕冰,葛泉波.一种基于分布式滤波的数据融合算法,电子学报.2004.08:1264-1267
[28]马忠孝,刘宗玉,陈明.基于模糊逻辑的自适应卡尔曼滤波在GPS/INS组合导航中的应用.信息与控制.2006.08:457-461
[29]王天荆,杨震,胡海峰.基于遗传算法的无线传感器网络自适应数据融合路由算法.电子与信息学报.2007.09:2244-2247
[30]李芳芳,宋晋敏.自适应数据融合算法.现代防御技术.2001.10:50-54
[31]Hong Luo,Jan Luo,Yonghe Liu.Adaptve Data Fusion for Energy Efficient Routiong in wireless Sensor Networks.IEEE transactions on computers.Vol.55,No.10 2006:1286-1299
[32]丁兴俊,周德云,胡昌华,王青.基于主被动雷达双传感器的自适应跟踪融合算法.弹箭与制导学报.2006.01:147-149
[33]Leonid A.Fokin.Multisensor Data Fusion in Adaptive Astro-Satellite-Inertial Navigation System.Siberian Confence on Control and Communications.2007:22-28
[34]黄鹤.机载多传感器数据融合的数据链应用技术.电讯技术,2004,(4):103-106
[35]孙红岩,毛士艺,林品兴.多传感器数据分层融合的性质.电子学报,1996,24(6):55-61
[36]王军,苏剑波,席裕庚.传感器融合综述.数据采集与处理,2004,19(1):72-77
[37]M.Dorigo,G.D.Lavo.Ant Algorithms for Discrete Opetimization.Artificial Life,1999,5(3):137-172
[38]Holland,J.H.Outline for a logical theory of adaptive systems.Journal of the Association for Computing Machinery,1962,9(3):297-314
[39]陈国良.并行计算一结构、算法、编程.北京:高等教育出版社,2001,62-71
[40]李士勇.蚁群算法及其应用.哈尔滨,哈尔滨工业大学出版社,2004,53-62
[41]http://www.iwr.uni-heidelberg.de/iwr/comopt/software/TSPLIB95
[42]M Dorigo,V Maniezzo,A colomi.The Ant system:Optimization by a Colony of Cooperationg Agents.IEEE Trans.On Systems Man Cybemet,1996,(B26):29-41
[43]吴斌,史忠植.一种基于蚁群算法TSP问题分段求解算法.计算机学报.2001,24(12):1328-1333
[44]R.Schoonderwoerd,O.Holland,J.Bruten.Ant-based Load Balancing in Telecommunications Networks.Adaptive Behavior.1997,5(2):169-207
[45]燕忠,袁春伟.用蚁群优化算法求解中国旅行商问题.电路与系统学报.2004,9(3):122-126
[46]L.Ma,F.Jiang.Ant Algorithm for Degree-constrained Minimum Spanning Tree.Journal of Systems Engineering.1999,14(3):211-214
[47]T.Stutzle,H.H.Hoos.MAX-MIN Ant System.Future Generation Computer Systems.2000,16(8):889-914
[48]J.H.Zhang,Q.S.Gao,X.H.Xu.A Self-adaptive Ant Colony Algorithm.Control Theory and Applications.2000,17(1):1-3
[49]李敏强、寇纪凇等,遗传算法的基本理论与应用.北京,科学出版社,2002.03:224-227
[50]T.Fukuda,K.Shimojima,E Arai,H.Matsuura.Multi-sensor integration system utilizing fuzzy inference and neural network.Information and Science,1992,71(1):27-41
[51]K.Shimojima,T.Fukuda,F.Arai,H.Matsuura.Fuzzy inference integrated 3-D measuring System with LED Displacement sensor and Vision System.Intelligent and Fuzzy Systems,1993,1(1):63-72
[52]Luo R.C.Dynamic Multisensor Data Fusion System for Intelligent Robots.IEEE Journal of Robotics and Automation.1988,4(4):386-396
[53]刘敏华,萧德云.基于相似度的多传感器数据融合.控制与决策.2004,9(5):534-537
[54]Luo R.C.,Min-Hsiung Lin,Ralph S.Scherp.The issues and approaches of arobot multisensor integration.IEEE International Conference on Robotics and Automation.1987,(3):1941-1946
[55]涂国平,邓群钊.多传感器数据的统计融合方法.传感器技术.2001,20(3):28-29,32
[56]卢大伟.多传感器数据融合方法在新型组合航姿系统中的应用.东南大学博士论文.2001.12:69-79
[57]蒲书缙,赵忠凯.基于一致理论的多源信息融合研究.应用科技.2006,33(2):13-15
[58]V.V.S.Sarma,Savithri Raju.Multisenor Data Fusion and Decision Support for Airbome Target identification.IEEE Transaction on Systems,Man,and Cybernetics.1991,21(5):1224-1230
[59]Thomas W.Automata on infinite objects.In:Lee wen JV ed.Handbook of Theoretical Computer Science.North-Holland:Else-vier Science Publisher.1990,(1):54-59
[60]Tenney R.R.,Sandell N.R.Detection with Distributed Sensors.IEEE Transactions on Aerospace and Electronic Systems,July 1981,17(4):561-510
[61]V.V.S.Sarma,Rao K.A.Decentralized Detection and Estimation in Distributed Sensor Systems.In Proceedings of the IEEE Cybernetics and Society Confenrence.1983,438-441
[62]Lauer G.S.,Sandell N.R.Distributed Detection with Waveform Observations:Correlated Observation Processes.Proceedings of the American Controls Conference.1981:812-819
[63]Sadjadi EA.Hypotheses testing in a Distributed Envrinments.IEEE Transactions on Aerospace and Electronic Systems.1986,22(1):134-137
[64]Chair Z.,Varshney P.K.Optimal Data Fusion in Multiple Sensor Detection Systems.IEEE Trans.on Aerospace and Electronic Systems.1986,22(A):98-101
[65]V.Venugopal,V.Veeravalli,T.Basar,H.V.Poor.Decentralized Sequential Detection with a Fusion Center Performing the Sequential Test.IEEE Transaction on Information Theory.1993,27:433-442
[66]V.Veeravalli,T.Basar,H.V.Poor.Mini max Robust Decentralized Detection.IEEE Transaction on Information Theory.1994,(38):35-40
[67]Kang Z.B.,K.R.Pattipati,D.K.K1einman.Optimization of Distributed Detection Networks.IEEE Transtaction on System,Man and Cybernatics.1993,23:211-221
[68]李宏.分布式多传感器数据融合理论、算法与应用.西北工业大学博士论文.2000.08:19-21
[69]Chen P.,A.Papamarcou.Error Bounds for Parallel distributed Detection under the Neyman -Pearson Criterion.IEEE Trans.on Info.Theory,March 1995,41:528-533
[70]Willet P.,Warren D.,Decentralized Detection:When are identical sensors identical.Proceedings of Conf.on Inform Sci.and Syst.,1991:287-292
[71]Tsitsiklis J.N.Decentralized Detection with A Large Number of Sensors.Mathematic Contr.And Signals Syst.1988,38:167-182
[72]Hashlamoun W.A.,Varshney EK.An Approach to Design of Distributed Bayesian Detection Structures.IEEE Trans.on Syst.,Man,and Cybernetics.1991,21(5):1206-1211,
[73]Thomopoulos SCA.,Viswanathan R.Globally optimal computable distributed detection fusion.The 26st IEEE Conference on Decoction and Control.1987:1846-1847
[74]Chair Z.,Ek.Varshney.Neyman-Pearson Hypothesis Testing in Distributed Networks.Proc.26th IEEE Trans.on Decision.and Control.1987:1842-1843
[75]Drakopoulos E.,C.C.Lee.Optimum Multisensor Fusion of Correlated Local Decisions.IEEE Trans.on Aerospace and Elect.Syst.1991,27(4):593-605
[76]Blum R.S.Necessary Conditions for Optimum Distributed Sensor Detectors under the Neyman-Pearson Criterion.IEEE Trans.on Info.Theory.1996,42(3):990-994
[77]Willett P.,D.Warren.The Suboptimality of Randomized Tests in Distributed and Quantized Detection Systems.IEEE Trans.on Info.Theory,March 1992,38(2):355-361
[78]孟伟,赵健等.基于神经网络与主观Bayes理论的雷达识别研究.计算机工程,2002,28(7):24-27
[79]孙红岩,张钱等.主观Bayes方法与神经网络相结合的多传感器数据融合空中点目标识别方法.红外与毫米波学报,1997,16(6):448-454
[80]涂国平,邓群钊.多传感器数据的统计融合方法.传感器技术,2001.20(3):28-29,32
[81]S.C.A.Thomopoulos,R.Viswanathan,D.Bougoulias.Optimal Decision Fusion in Multiple Sensor Systems.IEEE Trans.on AES,September.1987,AES-23(5):644-653
[82]D.M.Buede,E.L.Waltz.Benefits of Soft Sensors and Prohabilistic Fusion.SPIE Signal and Data Processing of Small Targets.1989,1096:309-320
[83]R.Krzysztofowicz,D.Long.Fusion of Detection Probabilities and Comparison of Multisensor Systems.IEEE Trans SMC.1990,20(3):665-677
[84]T.M.Strat.Continuous Belief Functions for Evidential Reasoning.Porcessing National Conference on Artificial Intelligence.1984:308-313
[85]L.A.Zadeh.Review of Books:A Mathematical Theory of Evidence.The AI Magazine.1984,5(3):81-83
[86]D.Dubois,H.Prude.Combination and Propagation of Uncertainty with Belief Functions-A Reexamination.Proceedings 9th International Joint Conference on Artificial Intelligence.1985:111-113
[87]R.M.Yannone,P.P.Bonissone.Attribute Fusion and Situation Assessment with a Many-Valued Logic Approach.Artech House INC.1992,2,Chapter1 1,
[88]D.Dubois,H.Prade.Criteria Aggregation and Ranking of Altemativesin the Framework of Fuzzy Set Theory.New York,Elsevier Science Publishers.1984,20:209-240
[89]J.H.Zhang,Q.S.Gao,X.H.Xu.A Self-adaptive Ant Colony Algorithm.Control Theory and Applications.2000,17(1):1-3
[90]Ralescu D.A,Adams G.The fuzzy integral.J.Math.Annual.1980,75:562-570
[91]赵汝怀.(N)模糊积分.数学研究与评论,1981,2:55-72
[92]张文修,赵汝怀.模糊测度与模糊积分的推广.模糊数学,1983,3:1-8
[93]Gareia F.S.,Alvarez P.G.Two families of fuzzy integrals.Fuzzy Sets and Systems.1986,18:67-81
[94]Garcia F.S.,Alvarez P.G.Measures of fuzziness of fuzzy events.Fuzzy Sets and Systems.1987,21:147-157
[95]Suzuki H.On fuzzy measures defined by fuzzy integrals.Math.Annual.1988,132:87-101
[96]张德利,郭彩梅,王中海.Fuzzy测度序列的收敛性和积分序列的收敛定理.东北师范大学学报(自).1993,4:11-14
[97]Ha M.H.,Wang X.Z.Weak convergence of fuzzy measure sequences.Fuzzy Math.1995,3:383-391
[98]Greco G.,Bassanezi R.C.On the continuity of fuzzy integrals.Fuzzy Sets and Systems.1993,53:87-91
[99]Flores H.R.,Franulic A.E On the level continuity of fuzzy integrals.Fuzzy Sets and Systems.1996,80:339-344
[100]Ralescu D.A.,Sugeno M.Fuzzy integral representation.Fuzzy Sets and Systems.1996,84: 127-133
[101]Squillante M.,Ventre A.Representations of the fuzzy integral.Fuzzy Sets and Systems,1989,29:165-169
[102]王子孝.模糊积分与模糊性度量.模糊数学,1982,1:57-68
[103]张广全.乘积空间上的SC-Fuzzy测度与Fuzzy积分.河北大学学报.1988,2:1-11
[104]何家儒.S型Fuzzy乘积测度与重积分.四川师范大学学报(自).1987,1:1-10
[105]Mesiar R.,Sipos J.Radon Nikodym like theorem for fuzzy measures.Fuzzy Math.1993,1:873-878
[106]Sugeno M.,Murofushi T.Pseudo-additive measures and integrals.Math.Annual.1987,122:197-222
[107]Benediktsson J.A.,Swain P.H.Consensus Theoretic Classification Methods.IEEE Trans.System an Cybemet.1992,(22):688-704
[108]Benediktsson J.A.,Sveinsson J.R.Optimied Consensus Theoretic.IEEE Trans.Geosci.Remote Sensing.1996:3490-3493
[109]K.C.Chang,R.K.Saha,Y.Bar-Shalom.On Optimal Track-to-Track Fusion.IEEE Trans.On AES.1997,33(4):1271-1276
[110]Zhou B.,Bose N.K.A comprehensive analysis of neural solution to the multi target tracking data association problem.IEEE Trans.AES.1993,29(6):260-263
[111]丁赤飚,毛士艺.基于雷达和成像传感器的融合跟踪.电子学报.1998,26(9):134-138
[112]G.J.Klir,B.Yuan,J.F.Swan-Stone.Constructing fuzzy measures from given data.Proc.6th IFSA Congress.1995:61-64
[113]J.Wang.Determining fuzzy measures by using statistics and neural networks.Proc.6th IFSA Congress.1995:519-521
[114]Keno Mynry Lee,Hyung Lee Kwang.Identification of A fuzzy measure by genetic algorithm s.Fuzzy Sets and Systems.1995,75(3):301-309