Multiagent Light Field Reconstruction and Maneuvering Target Recognition via GAN

Abstract EN

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.