基于高维特征选择的跳频电台细微特征识别

doi:10.3969/j.issn.1001-506X.2020.02.25

Abstract

Abstract:

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.

Key words: frequency hopping radio, subtle features, feature selection, support vector machine (SVM)

CLC Number:

TN911.7

Hongguang LI, Ying GUO, Ping SUI, Zisen QI, Linghua SU. Fine feature recognition of frequency hopping radio based on high dimensional feature selection[J]. Systems Engineering and Electronics, 2020, 42(2): 445-451.

Figures/Tables 7

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References 30

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分类特征	特征数	最优子集	正确率/%	时间/s
谱对称性均值f₁	1	{f₁}	43.51	0.008
谱对称性方差f₂	1	{f₂}	43.63	0.008
波形特征f₃	1	{f₃}	51.72	0.013
盒维数f₄	1	{f₄}	64.55	0.013
瑞利熵f₅	1	{f₅}	65.13	0.013
信息维数f₆	1	{f₆}	63.52	0.015
LZC f₇	1	{f₇}	63.43	0.013
常规特征	7	{f₁, f₂, f₃, f₄, f₅, f₆, f₇}	72.32	0.082
FCBF选择出的特征集	4	{f₃, f₄, f₆, f₇}	72.15	0.057
本文算法选择出的特征集	3	{f₄, f₅, f₇}	72.24	0.029

分类特征	特征维数	最优子集	正确率/%	时间/s
高阶谱特征SIB f₈	256	{f₈¹, f₈², …, f₈²⁵⁶}	80.60	1.715
FCBF选择出的特征集	193	{f₈², f₈⁶, …, f₈²⁵³}	82.41	0.774
本文算法选择出的特征集	148	{f₈³, f₈⁷, …, f₈²⁴⁶}	84.63	0.459

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

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