Abstract：Radar signal processing is one of the core contents of modern radar systems, which directly affects the scope and perfor-
mance of radar systems. The effective identification of radar signals is an important part of predicting unknown radar signals. This
paper uses the support vector machine (SVM) to intelligently identify four different radar signals, and selects the radial basis func-
tion (RBF) as the support vector’s non-linear mapping function. The theoretical derivation shows that the penalty factor c and the
kernel function parameter g affect its classification performance. This paper uses particle swarm optimization (PSO) to optimize two
important parameters of support vector machines. The results show that the classification performance of SVM without parameter
optimization is unstable and the recognition accuracy is between 79.6992% and 90.2256%, while the accuracy of SVM optimized by
particle swarm optimization algorithm is as high as 100%. The effectiveness of the optimization method is effectively proved, and
the accurate recognition of radar signals of SVM based on particle swarm optimization algorithm is realized
邓红源. 基于支持向量机的雷达信号特征的辨识研究[J]. 电脑与电信, .
DENG Hong-yuan. Research on Identification of Radar Signal Features Based on Support Vector
Machine Parameter Optimization. Computer & Telecommunication, 2020, 1(3): 44-46.
