基于高维特征选择的跳频电台细微特征识别

将高维特征用于跳频电台细微特征个体识别具有很大优势,为了增强对跳频电台的分类识别能力,需要增加特征类型和维数,提高特征集的表征能力,但同时会引入大量冗余特征,导致分类器计算时间过长,分类正确率降低。为了降低高维特征集维数,首先采用相关性快速过滤特征选择算法,删除高维特征集中的不相关冗余特征,得到最优特征集。然后利用经过参数优化的支持向量机(support vector machine, SVM)分类器进行训练分类。实验表明,所提算法能够对高维特征集进行合理的降维,提高了SVM的分类器的分类性能,在保证分类正确率的基础上,降低了运算量,提高了跳频电台细微特征识别的时效性。

Fine feature recognition of frequency hopping radio based on high dimensional feature selection

The high dimensional feature is used for individual identification of the fine features of the frequency hopping station. In order to enhance the classification and recognition ability of the frequency hopping station, it is usually necessary to increase the feature type and feature dimension of the feature set to improve the representation ability. However, many redundant features are introduced. As a result, the calculation time of the classifier is too long, and the classification correctness rate is lowered. In order to reduce the dimension of high-dimensional feature sets, the feature selection algorithm is firstly used to delete the irrelevant redundant features in the high-dimensional feature set to obtain the optimal feature set. Then, the parameter-optimized support vector machine (SVM) classifier is used for training and classification. Experiments show that the proposed algorithm can reduce the dimensionality of high-dimensional feature sets and improve the classification performance of SVM. On the basis of ensuring the correctness rate of classification, the computational complexity is reduced, and the timeliness of fine feature recognition of frequency hopping stations is improved.