Magnetic Field Feature Fusion of Underwater Vehicle Model Based on Principal Component Analysis

Abstract Aiming at the model of underwater vehicle, a magnetic field feature fusion method based on principal component analysis (PCA) is proposed, focusing on: Non-stationary, nonlinear and non-Gaussian time domain vehicle genetic algorithm optimization neural network Feature extraction technology, technology time-domain features, constructs effective PCA features with low dimension, finally uses sample recording data to conduct experiments main methods this paper Verification experimental simulation results show that can reduce dimension basis maintaining sufficient amount information, improve target recognition efficiency. The confirm effectiveness related algorithms.