Radar target classification based on support vector machines and high resolution range profiles

S. Kent*, N. G. Kasapoglu, M. Kartal

*Corresponding author for this work

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

10 Citations (Scopus)

Abstract

In this study, the support vector machine (SVM) was used as a classifier to identify aerospace objects. Radar target identification based on High Resolution Range Profiles (HRRPs) received much attention because of its reduced complexity than those using two-dimensional (2-D) ISAR images. Therefore range profiles were used as feature vectors to represent radar data. Data sets which are for training and testing were generated by using a program called radar target backscattering simulation (RTBS) for three different target types. The performance of the SVM was compared with other classification algorithms including statistical classification techniques such as maximum likelihood (ML) and fisher linear likelihood (FLL).

Original languageEnglish
Title of host publication2008 IEEE Radar Conference, RADAR 2008
DOIs
Publication statusPublished - 2008
Event2008 IEEE Radar Conference, RADAR 2008 - Rome, Italy
Duration: 26 May 200830 May 2008

Publication series

Name2008 IEEE Radar Conference, RADAR 2008

Conference

Conference2008 IEEE Radar Conference, RADAR 2008
Country/TerritoryItaly
CityRome
Period26/05/0830/05/08

Keywords

  • High resolution range profile (HRRP)
  • Support vector machine (SVM)
  • Target classification

Fingerprint

Dive into the research topics of 'Radar target classification based on support vector machines and high resolution range profiles'. Together they form a unique fingerprint.

Cite this