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DSmT理论在综合敌我识别中的应用 被引量:17

Application of DSmT in integrated identification of friend-or-foe
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摘要 战场环境的复杂性要求使用多种传感器对战场目标进行综合敌我识别,而综合敌我识别有待解决的基础性难题之一是如何对异类传感器输出的不确定性信息进行有效处理。针对一包含雷达、红外、电子支援措施和敌我识别器等传感器的综合敌我识别系统,对异类传感器敌我识别过程进行分析,根据其特点提出采用DSmT理论进行决策级融合,同时针对每类传感器构造不同的基本置信指派方法,并进行了算法仿真。实验结果证实了该方法的有效性。 Under a complex battlefield environment,the multi-sensor integrated identification of friend-or-foe based on multi-sensor information may be needed.The effective processing of uncertain information from different sensors is a fundamental problem to be solved.For the integrated system of friend-or-foe identification consists of radar,infrared,electronic support measure(ESM) and a device of friend or foe identification,the identification process of different sensors is analyzed.Then,the fusion rule based on Dezert-Smarandache theory(DSmT) is used to achieve the decision level fusion.At the same time,different methods for constructing the basic belief assignment function are introduced.The simulation results show the availability of this method.
出处 《系统工程与电子技术》 EI CSCD 北大核心 2010年第11期2385-2388,共4页 Systems Engineering and Electronics
关键词 决策层融合 综合敌我识别 DSMT 比例冲突再分配 decision level fusion integrated identification of friend-or-foe Dezert-Smarandache theory proportional conflicting redistribution
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参考文献15

  • 1Liggins M E,Hall D L,Llinas J.Handbook of multisensor data fusion theory and practice[M].2nd ed.CRC Press,2008.
  • 2郭建全,赵伟,黄松岭.一种改进的D-S证据合成规则[J].系统工程与电子技术,2009,31(3):606-609. 被引量:16
  • 3Dezert J,Smarandache F.Advances and applications of DSm T for information fusion[M].Rehoboth:American Research Press,2004.
  • 4Smarandache F,Dezert J.A simple proportional conflict redistribution rule[J].International Journal of Applied Mathematics and Statistics,2005,16(3):1-36.
  • 5Valin P,Djiknavorian P,Gremoer D.DSm theory for fusing highly conflicting ESM reports[C] //Proc.of 12th International Conference on Information Fusion,Seattle,2009:1211-1217.
  • 6Willem L,Norden W,Bolderheij F,et al,Combining system and user belief on classification using the DSmT[C] //Proc.of the 11th International Conference on Information Fusion,2008:768-775.
  • 7Laurence C.Using logic to understand relations between DSmT and DST[J].Computer Science,2009,5590(10):264-274.
  • 8Ristic B.Target identification using belief functions and implication rules[J].IEEE Trans.on Aerospace and Electronic Systems,2005,41(3):1097-1103.
  • 9Liu W.Analyzing the degree of conflict among belief functions[J].Artificial Intelligence,2006,170(11):909-924.
  • 10Pastina D,Spins C.Multi-feature based automatic recognition of ship targets in ISAR[J].Radar,Sonar & Navigation,2009,4(3):406-423.

二级参考文献18

  • 1张庆荣,单佩钧.雷达信号脉内特征分析的谱相关方法[J].电子对抗,1993(4):1-6. 被引量:9
  • 2ZHANGGexiang,JINWeidong,HULaizhao.Resemblance Coefficient Based Intrapulse Feature Extraction Approach for Radar Emitter Signals[J].Chinese Journal of Electronics,2005,14(2):337-341. 被引量:43
  • 3Yager R R. On the Dempster-Shafer framework and new combination rules[J]. Information System, 1989(4) :93 - 137.
  • 4Toshiyuki I. Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory[J]. IEEE Trans. on Reliability, 1991,40 (2) : 182 - 188.
  • 5Dubois D, Prade H. A set-theoretic view of belief functions: logical operations and approximations by fuzzy sets[J]. Int. J. GeneralSyst., 1986, 12: 193-26.
  • 6Takahiko H. Decision rule for pattern classification by integrating interval feature values[J]. Pattern Analysis and Machine Intelligence, 1998, 20 (4) :440 - 447.
  • 7Matsuyama T. Belief formation observation and belief integration using virtual belief space in Dempster-Shafer probability model[C]. Proc. of the IEEE on Multisensor Fusion and Integrating for Intelligent System, Los Vegas, NV, 1994:379 -386.
  • 8Campos F, Cavalcante S. An extended approach for DempsterSharer theory[C]. IEEE, 2003: 338 - 344.
  • 9Liu, Weiru. Analyzing the degree of conflict among belief functions[J]. Artificial Intelligence, 2006, 170(11) : 909 - 924.
  • 10Smets Ph. Decision making in the TBM: the necessity of the pignistic transformation[J]. International Journal of Approximate Reasoning, 2004, 38 : 133 - 147.

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