multiagent light field reconstruction and maneuvering target recognition via gan

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ID: 150282
2019
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Abstract
The lack of sample data and the limited visual range of a single agent during light field reconstruction affect the recognition of maneuvering targets. In view of the above problems, this paper introduces generative adversarial nets (GAN) into the field of light field reconstruction and proposes a multiagent light field reconstruction and target recognition method based on GAN. The algorithm of this paper utilizes the characteristics of GAN to generate data and enhance data, which greatly improves the accuracy of light field reconstruction. The consistency mean of all observations is obtained by multiagent data fusion, which ensures the reliability of sample data and the continuity of maneuvering target recognition. The experimental results show that the accuracy of light field reconstruction reaches 94.552%. The accuracy of maneuvering target recognition is 84.267%, and the more the agents are used, the shorter the recognition time.
Reference Key
luo2019mathematicalmultiagent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Peien Luo;Lei Cai;Guangfu Zhou;Zhenxue Chen
Journal journal of power sources
Year 2019
DOI
10.1155/2019/9710974
URL
http://dx.doi.org/10.1155/2019/9710974
Keywords
civil engineering (general)mathematics engineering (general)

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