论文标题
一种新颖的辐射识别方法
A Novel Approach to Radiometric Identification
论文作者
论文摘要
本文表明，使用Caponef功能工程方法，可以使用高度精确的辐射标识。我们在SDR收集的实验数据上测试了基本的ML分类算法。首先在点双重和皮尔逊相关系数的帮助下，然后使用p值对所建议特征的统计和相关性质进行分析。最相关的功能突出了。随机森林提供了99％的精度。我们对模型行为进行石灰描述。事实证明，即使特征空间的尺寸降低到3，仍然可以以99％的精度对设备进行分类。
This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR. The statistical and correlational properties of suggested features were analyzed first with the help of Point Biserial and Pearson Correlation Coefficients and then using P-values. The most relevant features were highlighted. Random Forest provided 99% accuracy. We give LIME description of model behavior. It turns out that even if the dimension of the feature space is reduced to 3, it is still possible to classify devices with 99% accuracy.
