Open AccessRadar data simulation using deep generative networks
Author(s) -
Song Yiheng,
Wang Yanhua,
Li Yang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0144
Subject(s) - radar , computer science , generative grammar , artificial neural network , generative model , representation (politics) , artificial intelligence , deep learning , data mining , telecommunications , politics , political science , law
Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground‐based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data.