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 match‍ing performance is established based on PCA and the GA-BP neural network for the automat‍ic 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.