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

XiaoFei Deng; WenYing Wang; YinHe Huang; Daoqing Wu

doi:10.1117/12.2684784

19 October 2023 A method of ship identification based on DBN and its performance simulation

XiaoFei Deng, WenYing Wang, YinHe Huang, Daoqing Wu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090L (2023) https://doi.org/10.1117/12.2684784
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

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.

Citation Download Citation

XiaoFei Deng, WenYing Wang, YinHe Huang, and Daoqing Wu "A method of ship identification based on DBN and its performance simulation", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090L (19 October 2023); https://doi.org/10.1117/12.2684784

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KEYWORDS

Target recognition

Education and training

Deep learning

Performance modeling

Data modeling

Machine learning

Neurons

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