基于投影网络的物理感知雷达图像合成

Qian Song, F. Xu, Xiao Xiang Zhu
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引用次数: 1

摘要

本文提出了一种新的网络模块——投影网络,将雷达的投影过程与可训练网络明确地结合起来。它假设每个二维雷达截面(RCS)图都是三维RCS图的投影。它将投影机制建模为一个可微层,这样它就可以与其他神经网络层(如卷积层和池化层)集成。该模型与雷达投影过程一致,因此考虑了中途停留等影响。它是专门为雷达应用而设计和使用的。本文将该网络应用于雷达图像合成,仿真结果显示了投影网络的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physical-aware Radar Image Synthesis with Projective Network
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.
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