Application of Feature Selection Based on GA-KPCA in Underwater Target Recognition

The complexity of underwater radiated acoustic field and underwater acoustic channel results in intercoupling of the noise signals received by sonar, modulation distortion, and strong nonlinearity. In this paper, the kernel function is used to map the nonlinear data of the original feature space to the high-dimensional feature space; the principal compo-nents analysis(PCA) method is used to extract the features from the high-dimensional feature space, and the genetic algorithm(GA) is used to optimize the kernel parameters, thus an underwater target feature selection method based on GA-kernel principal components analysis(KPCA) is established. Actual sample data validation shows that, to a certain extent, this method compensates the insufficiency of the traditional linear PCA method in dealing with nonlinear data, and it has higher recognition accuracy.