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Research on Emitter Recognition Model Based on Dempster-Shafer Evidence Theory

机译:基于Dempster-Shafer证据理论的辐射源识别模型研究

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In the battlefield environment, emitter information detected by multisensor takes on temporal redundancy. In order to solve emitter recognition problems in such practical reconnaissance environment, D-S reasoning method based on information fusion is applied. The key problem to D-S reasoning is basic probability assignment function, which to a great extent limit its applications. For the special traits of emitter recognition, a new method of constructing basic probability assignment function based on gray correlation analysis is present. Gray correlation coefficient between reference data array and comparable data array is calculated first, and then gray correlation degree set of multi-observation samples is given. Under the frame of Bayes belief, the new belief function is deduced. Examples of identifying the emitter type have been selected to demonstrate the new method. If four sensors are used, the new constructed basic probability assignment function enables the correct recognition rates to reach 98% in the simulation environment 1. Experimental results show that this information fusion method is accurate and effective and the correct recognition rates are improved evidently with the increasing number of sensors, especially with the increasing measurement noise.
机译:在战场环境中,多传感器检测到的发射器信息具有时间冗余性。为了解决这种实际侦察环境中的辐射源识别问题,应用了基于信息融合的D-S推理方法。 D-S推理的关键问题是基本的概率分配函数,这在很大程度上限制了其应用。针对辐射源识别的特点,提出了一种基于灰色关联分析的基本概率分配函数构造新方法。首先计算参考数据阵列和可比较数据阵列之间的灰色相关系数,然后给出多观测样本的灰色相关度集。在贝叶斯信念框架下,推导了新的信念函数。已选择识别发射器类型的示例来演示新方法。如果使用四个传感器,则新构建的基本概率分配函数可以使模拟环境1中的正确识别率达到98%。实验结果表明,这种信息融合方法是准确有效的,并且随着时间的推移,正确的识别率显着提高。传感器数量的增加,尤其是随着测量噪声的增加。

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