A Gravity Field Orientation Adaptability Analysis Method Based on Image Texture Features

By combining statistical features of the gravity field and image texture features, a parallel convolutional neural network model was designed, which solved the problems of error accumulation and discontinuous adaptation zone in gravity-assisted inertial navigation systems, and improved navigation accuracy and matching precision.

CN117710802BActive Publication Date: 2026-06-30BEIJING INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING INST OF TECH
Filing Date
2023-12-13
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing gravity-assisted inertial navigation systems suffer from error accumulation and discontinuous adaptation zone selection in underwater vehicles, making it difficult to meet the requirements of maneuvering navigation, and they do not consider direction adaptation information.

Method used

A gravity field orientation adaptability analysis method based on image texture features is adopted. By calculating the statistical feature parameters of the gravity field and the feature parameters of the image texture, a parallel convolutional neural network model is designed to analyze the gravity field adaptation region.

Benefits of technology

It improves the navigation accuracy and multi-directional matching precision of gravity-assisted inertial navigation systems, with more continuous selected adaptation areas, obvious texture features, and improved classification accuracy.

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Abstract

This invention discloses a gravity field orientation adaptability analysis method based on image texture features. This method can select adaptability regions rich in gravity information, providing directional guidance for gravity field trajectory planning and effectively improving the accuracy of multi-directional gravity field matching. This invention comprehensively considers both gravity field statistical feature parameters and image texture feature parameters, fusing these two features to determine the adaptability region, making the selected adaptability region more continuous and the texture features more obvious. During dataset training, a parallel convolutional neural network is used, simultaneously training statistical feature parameters reflecting gravity field statistical features and gray-level gradient co-occurrence matrix parameters reflecting image texture features. These features are mapped to the output layer for classification, and the gravity field adaptability region is determined based on the classification results, thus achieving the evaluation of the orientation adaptability of the gravity field adaptability region.
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