Study on emitter signal recognition based on backward cloud model and attribute similarity

Pan Li-na
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Abstract

To deal with the problem of radar emitter recognition caused by noise environment, this paper presents a new method for emitter recognition based on backward cloud model and attribute similarity. First, it constructs a radar emitter database included with noise data according with the reality, then calculates the cloud numerical characteristic based on backward cloud model, presents a method of determining recognition weight of coefficients, and a new classification implement based on backward cloud model and attribute similarity is proposed. Simulation results show that the method proposed by this article can deal with the randomicity and vagueness caused by noise environment much better and can conduct emitter recognition effectively in the adverse noise envioroment.
基于后向云模型和属性相似度的发射机信号识别研究
针对噪声环境对雷达辐射源识别的影响,提出了一种基于后向云模型和属性相似度的辐射源识别新方法。首先根据实际情况构建了包含噪声数据的雷达辐射源数据库,然后基于后向云模型计算了云数值特征,提出了一种确定系数识别权值的方法,并提出了一种基于后向云模型和属性相似度的分类实现方法。仿真结果表明,本文提出的方法能较好地处理噪声环境带来的随机性和模糊性,能在不利的噪声环境下有效地进行辐射源识别。
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