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战场目标识别中的D-S证据理论应用 被引量:3

Application of Dempster-Shafer evidence theory in battlefield object recognition
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摘要 为了解决干扰情况下地震动信号发射源的定性问题,提出了在决策层上的多传感器数据融合的识别方法。利用定位传感器组中各个传感器得到的数据,并考虑在同一个定位组中各个传感器所得数据的置信度不同,来对地震动信号发射源性质进行识别。实验结果证明:基于D-S证据理论的战场目标识别数据融合后,地震动信号发射源特性识别的可靠性明显大于单个传感器的识别效果,这也表明了多传感器融合识别的可能性和有效性。 In order to resolve problem of the recognition of ME source, the method of multi-sensor data fusion on decision level is submitd. Using the data from each sensor in a location set and considering the confidence value for each sensor, the ME source characteristics can be acquired. Experiment results show that the battlefield object recognition based on Dempster-Shafer evidence theory' s data fusion result is better than that of single sensor, and the multi-sensor data fusion method is effective.
出处 《传感器与微系统》 CSCD 北大核心 2007年第11期111-114,共4页 Transducer and Microsystem Technologies
关键词 地震动信号 数据融合 目标识别 Dempster—Shafer(D—S)证据推理法 特征提取 基本概率赋值 microseismic signals data fusion object recognition Dempster-Shafer ( D-S ) evidence theory feature extracting basic probability assignment
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  • 1Dempster A P. Upper and lower probabilities induced by a multivalued mapping[J]. Annals of Mathematical Statistics,1967(38) : 325 -339.
  • 2Seizer F, Gutfinger D. LADAR and FLIR based sensor fusion for automatic target classification[ J ]. SPIE( Society of Photo-Optical Instrumentation Engineers) ,1988( 1003 ) :236 --246.
  • 3Yair Shimshoni, Nathan Intrator. Classification of seismic signals by integrating ensembles of neural networks [ J]. IEEE Transactions on Signal Processing, 1998,46 (5) : 1194 --1201.

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