基于最大分类间隔SVDD算法的辐射源个体确认
doi: 10.3724/SP.J.1146.2011.00103
Specific Emitter Verification Based on Maximal Classification Margin SVDD
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摘要: 通信辐射源个体确认技术是实现通信辐射源个体识别的关键技术之一。该文研究了基于支持向量数据描述(SVDD)的通信辐射源个体确认算法。针对传统SVDD算法在正类训练样本不完备的条件下对正类测试样本接受率较低的不足，提出带反类训练的最大分类间隔SVDD算法(MCM-SVDD)。MCM-SVDD在保证最小化超球体积的同时，使正类训练样本与反类训练样本距离超球表面的间隔最大化，从而提高了对正类测试样本正确接受的泛化能力。基于20台实际通信辐射源样本的实验表明，相对于SVDD, SVDD-neg和SVM, MCM-SVDD具有更高的平均确认率。
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关键词:
- 无线通信 /
- 辐射源个体确认 /
- 支持向量数据描述 /
- 最大分类间隔SVDD /
- 辐射源指纹
Abstract: Specific Emitter Verification (SEV) is one of the key technology to identify a specific emitter. Specific Emitter Verification algorithm based on Support Vector Data Description (SVDD) is studied in this paper. To improve the low fraction of target class that is accepted by the classical SVDD in the case of atypical target training data, Maximal Classification Margin SVDD (MCM-SVDD) using outlier training data is proposed. At the same time that the margin is maximized between hyper-sphere and target training data as well as outlier training data, hyper-sphere volume is minimized by MCM-SVDD to improve the generalization of target data accepting. By experiment on data from 20 real communication emitters, MCM-SVDD is proved to perform better mean verification rate than SVDD, SVDD-neg and SVM. -
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