Abstract: To improve the accuracy and computational efficiency of specific emitter identification,a specific emitter identification based on hybrid feature selection is proposed.Wrapped feature selection methods have high classification accuracy,but it has high computational complexity and low efficiency in processing high-dimensional data.Embedded feature selection methods have low computational complexity,but rely on specific classifiers.To address the above problems,combining the characteristics of wrapped and embedded feature selection methods,firstly,three embedded methods(Random Forest,XGBoost,and LightGBM) are used to initially select features for signal data,and a random forest subset,an XGBoost subset and a LightGBM subset are obtained respectively.Secondly,the wrapped methods are used to perform a second dimensionality reduction on the subset obtained after the primary selection.Sequential backward selection and an ant colony optimization algorithm are used as research strategies respectively,while LightGBM is used as the classification algorithm.A total of six feature selection models are obtained from the proposed hybrid feature selection method.The optimal hybrid feature selection model is determined by comparing the classification accuracy and the number of features in the optimal subset obtained by each model.

Key words: Specific emitter identification, Feature selection, Random forest, XGBoost, LightGBM, Sequential backward selection, Ant colony optimization