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PSO-CNN-Based Initial Alignment for Fiber Optic Gyroscope
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Version 1
: Received: 3 February 2024 / Approved: 4 February 2024 / Online: 5 February 2024 (06:30:27 CET)
How to cite: Zhang, H.; Huang, D. PSO-CNN-Based Initial Alignment for Fiber Optic Gyroscope. Preprints 2024, 2024020194. https://doi.org/10.20944/preprints202402.0194.v1 Zhang, H.; Huang, D. PSO-CNN-Based Initial Alignment for Fiber Optic Gyroscope. Preprints 2024, 2024020194. https://doi.org/10.20944/preprints202402.0194.v1
Abstract
The exceptional performance advantages of the fiber optic gyroscope (FOG) position it as a dominant player in middle and high-end inertial navigation systems. To prevent the loss of sensor precision caused by algorithm design and simplify the complex modeling strategy in traditional methods. We gradually demonstrate the significant role of Convolutional Neural Network (CNN) in the navigation system based on FOG, and utilize the particle swarm optimization algorithm (PSO) to expedite the convergence of the network. The experimental results demonstrate that the initial alignment method based on deep learning is more accurate than the traditional method. The attitude angle error is reduced by 81.25%, 92.54% and 36.53% respectively. The research provides support for the future application of deep learning in optical navigation systems.
Keywords
initial alignment; fiber optic gyroscope; convolutional neural network; particle swarm optimization algorithm
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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