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XiaoFei Deng

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Proceedings Article | 19 October 2023 Paper

A method of ship identification based on DBN and its performance simulation

XiaoFei Deng, WenYing Wang, YinHe Huang, Daoqing Wu

Proceedings Volume 12709, 127090L (2023) https://doi.org/10.1117/12.2684784

KEYWORDS: Target recognition, Education and training, Deep learning, Performance modeling, Data modeling, Neurons, Machine learning, Synthetic aperture radar

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As an important branch of artificial intelligence, deep learning network has made remarkable achievements in image recognition, target analysis and other fields in recent years. At the same time, HRRP has been widely used in target recognition, especially in ship target recognition, because its data is easy to obtain and process and contains more target information. Under the above background, this paper proposes a target recognition method based on DBN network, and analyzes and verifies the performance of the network with the measured data of ten types of military and civilian ship targets. Through the experiments on SVM and DBN in low bandwidth, it is found that DBN model can maintain a high recognition rate even when the amount of target information is reduced, and has a certain robustness. When the attitude angle changes, the recognition performance of DBN model is relatively stable, which overcomes the problem of attitude angle sensitivity to a certain extent, and has a good application prospect.

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