Compressive sensing of up-sampled model and atomic norm for super-resolution radar

Dongshin Yang, Yutaka Jitsumatsu

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

2 Citations (Scopus)


Compressive sensing (CS) for radar signal processing is known to be capable of various applications. This signal processing technique shows excellent performance for detecting objects. However, the grid problem of CS is an obstacle to more precise performance. In this paper, we introduce two methods to overcome this grid problem and evaluate the performance of the methods. The first method is an up-sampled model, which is a method of dividing the grids into smaller pieces. The second method is an atomic norm minimization, which is a detectable method for continuous parameters.

Original languageEnglish
Title of host publication2017 18th International Radar Symposium, IRS 2017
EditorsHermann Rohling
PublisherIEEE Computer Society
ISBN (Electronic)9783736993433
Publication statusPublished - Aug 10 2017
Event18th International Radar Symposium, IRS 2017 - Prague, Czech Republic
Duration: Jun 28 2017Jun 30 2017

Publication series

NameProceedings International Radar Symposium
ISSN (Print)2155-5753


Other18th International Radar Symposium, IRS 2017
Country/TerritoryCzech Republic

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Astronomy and Astrophysics
  • Instrumentation


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