Feature Extraction and Reconnaissance Algorithm of Radar Signal Based on Support Vector Machine Learning

Machine learning (ML) can automatically extract features from a large number of data, and accurately predict and classify unknown data by training models. This article focuses on the research of radar signal feature extraction and reconnaissance algorithm based on ML, and pays special attention to the application of fuzzy support vector machine (FSVM) in radar signal reconnaissance. By using FSVM algorithm, it aims to solve the limitations of traditional radar signal reconnaissance methods in the face of complex and changeable signals. This article introduces the principle of FSVM algorithm, and combines the characteristics of radar signal to construct an efficient feature extraction and reconnaissance algorithm. The experimental part focuses on the influence of kernel parameter selection on model performance, and verifies the effectiveness of over-fitting measures in improving accuracy. The research results show that compared with the traditional radar signal reconnaissance algorithm, the algorithm based on FSVM proposed in this article has achieved significant improvement in accuracy and response time, which proves the superiority of this method in radar signal feature extraction and reconnaissance tasks.