Radar signal recognition is a key step of electronic reconnaissance. In order to use various parameters of radar signal to identify its country in complex electromagnetic environment, this paper analyses the characteristics of radar signal and the development status of related recognition methods. Aiming at the support vector machine (SVM) classification algorithm in machine learning, this paper studies the feature selection and the establishment, evaluation and optimization of model, and completes the programming and application of the algorithm, the recognition accuracy and efficiency are improved. The research shows that when SVM is applied to radar signal recognition, the classification accuracy is more than 93% and the AUC value is more than 0.99.
KEYWORDS: Data modeling, Radar, Machine learning, Detection and tracking algorithms, Statistical modeling, Optimization (mathematics), Chaos, Radar signal processing, Data processing, Classification systems
In order to classify and identify the countries of radar signals, this paper analyzes the characteristics and current situation of radar signal recognition, adopts the decision tree classification algorithm in machine learning, studies the data preprocessing and the construction, evaluation and optimization of decision tree model, and realizes the program design and practical application of decision tree classification algorithm, which provides effective support for improving the efficiency of radar reconnaissance and early warning. The research shows that the decision tree model can effectively classify and recognize radar signals after training and optimization, with an accuracy of more than 96% and an AUC value of more than 0.98.
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