基于蚁群参数优化的LightGBM辐射源个体识别

为提升辐射源个体识别正确率和运算效率，提出了一种基于蚁群参数优化的LightGBM辐射源个体识别方法。运用提升小波包变换对辐射源信号数据进行特征提取并构建特征参数体系，对得到的特征数据集进行Z-score标准化处理；以最大分类正确率和最小特征子集规模为目标，建立了LightGBM参数优化和特征选择的数学模型；采用蚁群算法优化LightGBM的6个参数(最小叶子节点数据量、决策树的数量、学习率、L₁正则化项的权重、L₂正则化项的权重和最小叶子节点样本权重和);根据优化后的LightGBM得到每个特征的重要性值并使用序列后向搜索策略进行特征选择；最后通过LightGBM分类器实现对辐射源信号的分类识别。实验结果表明，所提方法在无噪声、信噪比为10 dB和信噪比为5 dB信号的数据集上的识别正确率优于对比特征选择方法GBDT、XGBoost和LightGBM的，同时特征维数的减少也提升了辐射源个体识别的运算效率。

Specific emitter identification of LightGBMbased on ant colony parameters optimization

In order to improve the accuracy and efficiency of specific emitter identification, a specific emitter identification method of LightGBM based on ant colony parameters optimization is proposed. The lifting wavelet packet transform is used to extract the characteristics of the emitter signal data and construct the characteristic parameter system. The obtained characteristic data set is processed by Z-score standardization. Aiming at the maximum classification accuracy and the minimum feature subset size, a mathematical model of LightGBM parameter optimization and feature selection is established. The ant colony optimization is used to optimize the six parameters of LightGBM (minimum leaf node data volume, number of decision trees, learning rate, L1 regularization item weight, L2 regularization item weight and minimum leaf node sample weight sum). According to the optimized LightGBM, the importance value of each feature is obtained, and the sequential backward search strategy is used for feature selection. The identification of emitter signals is realized through the LightGBM classifier. The experimental results show that the recognition accuracy of the proposed method is better than the comparative feature selection methods (GBDT, XGBoost and LightGBM) on the signal data set with no noise, signal-to-noise ratio of 10dB and signal-to-noise ratio of 5dB. At the same time, the reduction of feature dimension also improves the computational efficiency of specific emitter identification.