Comprehensive Image Matching Algorithm Based on Local GLCM for Gravity-Gradient-Aided Navigation

Matching algorithm is the key technology of gravity-gradient-aided inertial navigation system (INS). Traditional single-point and sequence matching algorithms fail to make full use of the multicomponent gravity gradient reference map information, so that the matching accuracy cannot meet the requirements. A gravity gradient comprehensive image matching algorithm based on local gray-level co-occurrence matrix (GLCM) is proposed. Gravity gradient reference maps are prepared by using the frequency-domain Fourier transform method. Gravity gradient real-time images are constructed within the confidence interval of INS. The state equation and observation equation are established. The optimal matching image is determined by synthesizing the gravity gradients in five independent directions through the similarity coarse screening and local GLCM feature matching. Finally, the matching position coordinates are calculated in reverse. Simulation and experimental results show that the matching accuracy of the proposed algorithm is within one grid in both short-term and long-term matching errors. It is proved that the proposed algorithm not only has high accuracy and better robustness, but also has no strict requirements on the change of gravity gradient characteristics in the navigation area.

Automated summary

A gravity gradient comprehensive image matching algorithm based on local gray-level co-occurrence matrix (GLCM) is proposed, which can make better use of multicomponent gravity gradient reference map information and improve matching accuracy. The algorithm has high accuracy and better robustness, and does not have strict requirements on the change of gravity gradient characteristics in the navigation area.