Geomagnetic Vector Pattern Recognition Navigation Method Based on Probabilistic Neural Network
Zhuo Chen, Kunjia Liu, Qi Zhang, Zhongyan Liu, Dixiang Chen, Mengchun Pan, Jiafei Hu, Yujing Xu
Abstract
Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.