针对地磁导航方向适配性分析时人工提取的特征主观性较强且难以表达深层的结构性特征的问题,提出一种基于深度卷积神经网络(convolutional neural network,CNN)的地磁导航方向适配性分析方法.首先,利用Gabor滤波器的方向选择特性建立了6个典型方向的适配特征图;然后,设计了卷积神经网络对深层次的方向适配特征进行提取,并通过混和粒子群算法(hybrid particle swarm optimization,HPSO)对卷积神经网络的训练参数进行优选;最后,通过仿真实验对所提方法进行了验证.结果表明,该方法可有效避免复杂的计算以及人工特征提取的盲目性,实现了地磁导航方向适配性分析的自动化,且所提方法的准确率高于传统的BP网络和支持向量机,对地磁导航和航迹规划具有指导意义.
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.