A Sparsity Based CFAR Algorithm for Dense Radar Targets

Jingxuan Chen, Shenghua Zhou, Pramod K. Varshney, Jing Lu, Jibin Zheng, Hongwei Liu, Hongtao Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Conventional constant false alarm rate (CFAR) radar target detection algorithms, such as the cell-averaging (CA) CFAR and the order statistic (OS) CFAR, performs poorly with dense targets. In this paper, we develop a sparse signal processing-based CFAR algorithm for dense target detection. Under the ℓ1-norm constraint, we develop the relationship between the coefficient and the false alarm rate and verify the false alarm rate via numerical simulations. Numerical results indicate that the sparse signal processing-based method performs better in dense targets scenarios than CA-CFAR and OS-CFAR. The computation cost is evaluated both theoretically and numerically. We show that the computation time of MM-CFAR and CA-CFAR algorithms increases nearly linearly with the sample size.

Original languageEnglish (US)
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189420
DOIs
StatePublished - Sep 21 2020
Event2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
Duration: Sep 21 2020Sep 25 2020

Publication series

NameIEEE National Radar Conference - Proceedings
Volume2020-September
ISSN (Print)1097-5659

Conference

Conference2020 IEEE Radar Conference, RadarConf 2020
Country/TerritoryItaly
CityFlorence
Period9/21/209/25/20

Keywords

  • CFAR
  • Sparse signal processing
  • majorization-minimization
  • radar target detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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