Physical-aware Radar Image Synthesis with Projective Network

Qian Song, Feng Xu, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper proposed a new network module named as projection network, which explicitly combined radar's projection process with trainable network. It assumes that each 2D radar cross section (RCS) map is a projection of a 3D RCS map. And it models the projection mechanism as a differentiable layer, so that it can be integrated with other neural network layers, such as convolutional and pooling layers. The proposed model is consistent with radar projection process, hence effects such as layover is considered. It is designed and used specifically for radar applications. This paper applied the proposed network on radar image synthesis, and the simulation results showed great potential of projective network.

Original languageEnglish
Title of host publication2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789463968027
DOIs
StatePublished - 28 Aug 2021
Externally publishedYes
Event34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021 - Rome, Italy
Duration: 28 Aug 20214 Sep 2021

Publication series

Name2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

Conference

Conference34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021
Country/TerritoryItaly
CityRome
Period28/08/214/09/21

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