Direction-matching-suitability analysis for geomagnetic navigation based on convolutional neural networks

Aimed at the problems of artificial direction matching features being too subjective to analyze magnetic matching suitability and deep architectural features that can't be extracted, a new matching suitability analysis method based on a convolutional neural network (CNN) is proposed. First, direction-matching-suitability feature maps in six typical directions are established using the Gabor filter's direction selection characteristics. Second, a CNN is designed to extract the deep direction features. The training parameters of the CNN are optimized with a hybrid particle swarm optimization (HPSO) algorithm. Finally, simulation experiments are conducted to verify the proposed method. Results show that the method can effectively avoid complicated calculations and blindness when artificially extracting direction features, and the direction-matching-suitability analysis for magnetic navigation can be achieved automatically. The method's analysis accuracy is higher than in the traditional BP neural network (BPNN) and support vector machine (SVM), and has practical implications for geomagnetic navigation and route planning.