Dimension-Expanded-Based Matching Method With Siamese Convolutional Neural Networks for Gravity-Aided Navigation

Abstract

Matching algorithm is the key technique of the gravity-aided inertial navigation system. With the development of artificial intelligence, many neural network-based matching methods have been extensively studied. The pattern recognition-based matching methods transforms the matching problem as pattern recognition, which cannot be used directly on datasets where the neural networks have not been trained. To improve the accuracy of navigation and positioning, it is necessary to extract muti-dimensional gravity features from the limited navigation information. In this paper, the sequence of the gravity anomaly value is expanded to two-dimensional feature map containing time series features by Gramian Angular Fields method, which preserves the numerical information of the one-dimensional sequence and extracts the correlation relationship between each element. In addition, to reduce the influence of gravity measurement instrument error on the position precision of gravity matching algorithm, affine transformation is performed on INS trajectory and a Siamese convolutional neural network model is proposed to compare the measured gravity database with the gravity anomaly value in the pre-stored gravity background map and get the matching position. Simulation results and practical tests show that the proposed method can obtain a more precise location result compared with the traditional matching algorithm.