A method of identifying electromagnetic radiation sources by using support vector machines
A method of identifying electromagnetic radiation sources by using support vector machines
复制标题
一种利用支持向量机识别电磁辐射源的方法
DOI：
10.1109/cc.2013.6570798
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发表时间：
2013-07
期刊：
影响因子：
--
通讯作者：
Gao Yougang
中科院分区：
文献类型：
--
作者：
Shi Dan;Gao Yougang
电磁辐射源识别技术是一项广泛应用于军事、辐射管理和电磁干扰诊断的关键技术。机器学习方法的辨别能力最近已被用于促进ERSI。本文提出了一种新的方法来改善ERSI，采用支持向量机，这被证明是有效的工具，模式分类和回归的基础上，电磁辐射源的空间分布。为了简化模型，空间信息从3D立方体转换为1D矢量，其中下标作为输入。该模型使用187500个数据集进行训练，以使其能够识别辐射源类型，准确率高达99.9%。参数的影响（例如，惩罚参数，来自周围环境的反射和噪声，以及输入数据的缩放方法）进行了讨论。该方法在噪声和混响环境中具有良好的性能。在信噪比为20 dB时，识别准确率为82.15%。该方法在噪声环境中比人工神经网络具有更好的精度。考虑到每个电磁源具有独特的空间特征，该方法可用于电磁源识别和电磁干扰诊断。
Electromagnetic Radiation Source Identification (ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics. The discriminative capability of machine learning methods has recently been used for facilitating ERSI. This paper presents a new approach to improve ERSI by adopting support vector machines, which are proven to be effective tools in pattern classification and regression, on the basis of the spatial distribution of electromagnetic radiation sources. Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model. The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%. The influence of parameters (e.g., penalty parameter, reflection and noise from the ambient environment, and the scaling method for the input data) are discussed. The proposed method has good performance in noisy and reverberant environment. It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB. The proposed method has better accuracy in a noisy environment than artificial neural networks. Given that each Electromagnetic (EM) source has unique spatial characteristics, this method can be used for EM source identification and EM interference diagnostics.
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DOI：
10.1109/icinfa.2008.4608087
发表时间：
2008-06
期刊：
2008 International Conference on Information and Automation
影响因子：
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影响因子：
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期刊：
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期刊：
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影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
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