自组织优化分类的AUV地磁导航适配区选取

为确保水下自主航行器(autonomous underwater vehicle, AUV)地磁导航的可靠性及其航迹规划的合理性，提出了一种基于主成分分析(principal component analysis, PCA)和改进反向传播(back-propagation, BP)神经网络结合的候选地磁匹配区自组织优化分类方法。将候选地磁匹配区的分类问题统一在模式识别的框架下，首先，采用PCA对若干地磁图特征参数进行线性变换，获取独立的主成分特征参量；然后，利用遗传算法(genetic algorithm, GA)优化BP神经网络的初始权阈值来提高候选地磁匹配区适配性分类的准确性；最后，借助GA-BP神经网络来构建地磁图特征参数和匹配性能的映射关系，完成地磁适配区的自动识别。仿真实验结果表明，该自组织优化分类方法在地磁导航适配区选取方面具有较高的分类精度和可靠性，为AUV的高精度长航时自主导航提供重要保障。

Matching Area Selection for AUV Geomagnetic Navigation by Self-organizing Optimization Classification

  Objectives  For the reliability of the geomagnetic navigation of an autonomous underwater vehicle (AUV) and the rationality of route planning, a self-organizing optimal classification method based on principal component analysis (PCA) and the improved back-propagation (BP) neural network is proposed for candidate geomagnetic matching areas.  Methods  This paper unifies the classification of candidate geomagnetic matching areas into the framework of pattern recognition. Firstly, PCA is used to linearly transform some geomagnetic characteristic parameters to obtain the independent characteristic parameters of principal components. Secondly, the initial weights and thresholds of the BP neural network are optimized by the genetic algorithm (GA) to improve the classification accuracy of the matching suitability of candidate geomagnetic matching areas. Finally, the correspondence between the geomagnetic characteristic parameters and match?ing performance is established based on PCA and the GA-BP neural network for the automat?ic recognition of geomagnetic matching areas.  Results  Simulated experimental results show that the self-organizing optimization classification method has a higher classification accuracy and reliability in the selection of the matching areas for geomagnetic navigation and the accuracy of integrated navigation systems is also improved.  Conclusions  The proposed method can provide important support for AUV route planning, which is an effective guarantee for the high-precision and long-voyage autonomous navigation of AUVs.