Intelligent Classification of Spatial Targets Based on RCS Sequences

Liang Zhang, Bowen Xue, Yangbo Zhou, Yunchuan Wang, Xiaomin Pan

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

1 Citation (Scopus)

Abstract

Using the radar cross section (RCS) sequence of spatial moving targets with different azimuth, elevation angles, and poses, classification of spatial targets are achieved according to their two-dimensional images by deep residual networks. The numerical results show that the two-dimensional radar images can contain sufficient information of the spatial targets and thus exploited by the deep residual networks to achieve good classification.

Original languageEnglish
Title of host publication2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781733509657
DOIs
Publication statusPublished - 2023
Event2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023 - Hangzhou, China
Duration: 15 Aug 202318 Aug 2023

Publication series

Name2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

Conference

Conference2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023
Country/TerritoryChina
CityHangzhou
Period15/08/2318/08/23

Keywords

  • Radar RCS
  • ResNet
  • Space target imaging
  • Target classification

Fingerprint

Dive into the research topics of 'Intelligent Classification of Spatial Targets Based on RCS Sequences'. Together they form a unique fingerprint.

Cite this