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

Dongshin Yang, Yutaka Jitsumatsu

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2017 18th International Radar Symposium, IRS 2017
編集者Hermann Rohling
出版社IEEE Computer Society
ISBN(電子版)9783736993433
DOI
出版ステータス出版済み - 8 10 2017
イベント18th International Radar Symposium, IRS 2017 - Prague, チェコ共和国
継続期間: 6 28 20176 30 2017

出版物シリーズ

名前Proceedings International Radar Symposium
ISSN(印刷版)2155-5753

その他

その他18th International Radar Symposium, IRS 2017
Countryチェコ共和国
CityPrague
Period6/28/176/30/17

All Science Journal Classification (ASJC) codes

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

フィンガープリント 「Compressive sensing of up-sampled model and atomic norm for super-resolution radar」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル