Space object identification based on narrowband radar cross section

Cai Wang*, Xiongjun Fu, Qian Zhang, Long Jiao, Meiguo Gao

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

A space object identification method based on narrowband radar cross section (RCS) is proposed in this paper. With RCS sequences, statistical features are extracted, the two-dimensional sizes of the object are estimated by adopting an improved size estimation model (SEM) and the attitude stability of the object is determined by periodicity analysis. Feature templates are constructed in matrix form with those three kinds of features of the training sample set and especially when constructing the template for a large number of statistical features, dynamic fuzzy clustering technology is used. The k-nearest neighbor fuzzy classifier is adopted to accomplish the identification. At the end, experiments with the measured data are conducted to confirm the effectiveness of the method.

Original languageEnglish
Title of host publication2012 5th International Congress on Image and Signal Processing, CISP 2012
Pages1653-1657
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 5th International Congress on Image and Signal Processing, CISP 2012 - Chongqing, China
Duration: 16 Oct 201218 Oct 2012

Publication series

Name2012 5th International Congress on Image and Signal Processing, CISP 2012

Conference

Conference2012 5th International Congress on Image and Signal Processing, CISP 2012
Country/TerritoryChina
CityChongqing
Period16/10/1218/10/12

Keywords

  • dynamic fuzzy clustering
  • feature extraction
  • k-nearest neighbor fuzzy classifier
  • radar cross section
  • space object identification

Fingerprint

Dive into the research topics of 'Space object identification based on narrowband radar cross section'. Together they form a unique fingerprint.

Cite this