Abstract: 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.

Key words: specific emitter identification, lifting wavelet package transform, ant colony optimization, LightGBM, feature selection