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