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)

Abstract

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
DOIs
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

Other

Other18th International Radar Symposium, IRS 2017
CountryCzech Republic
CityPrague
Period6/28/176/30/17

Fingerprint

radar resolution
norms
Signal processing
Radar
grids
signal processing
radar
optimization
Radar signal processing

All Science Journal Classification (ASJC) codes

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

Cite this

Yang, D., & Jitsumatsu, Y. (2017). Compressive sensing of up-sampled model and atomic norm for super-resolution radar. In H. Rohling (Ed.), 2017 18th International Radar Symposium, IRS 2017 [8008110] (Proceedings International Radar Symposium). IEEE Computer Society. https://doi.org/10.23919/IRS.2017.8008110

Compressive sensing of up-sampled model and atomic norm for super-resolution radar. / Yang, Dongshin; Jitsumatsu, Yutaka.

2017 18th International Radar Symposium, IRS 2017. ed. / Hermann Rohling. IEEE Computer Society, 2017. 8008110 (Proceedings International Radar Symposium).

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

Yang, D & Jitsumatsu, Y 2017, Compressive sensing of up-sampled model and atomic norm for super-resolution radar. in H Rohling (ed.), 2017 18th International Radar Symposium, IRS 2017., 8008110, Proceedings International Radar Symposium, IEEE Computer Society, 18th International Radar Symposium, IRS 2017, Prague, Czech Republic, 6/28/17. https://doi.org/10.23919/IRS.2017.8008110
Yang D, Jitsumatsu Y. Compressive sensing of up-sampled model and atomic norm for super-resolution radar. In Rohling H, editor, 2017 18th International Radar Symposium, IRS 2017. IEEE Computer Society. 2017. 8008110. (Proceedings International Radar Symposium). https://doi.org/10.23919/IRS.2017.8008110
Yang, Dongshin ; Jitsumatsu, Yutaka. / Compressive sensing of up-sampled model and atomic norm for super-resolution radar. 2017 18th International Radar Symposium, IRS 2017. editor / Hermann Rohling. IEEE Computer Society, 2017. (Proceedings International Radar Symposium).
@inproceedings{09e34433dc464e93be98f90921537e95,
title = "Compressive sensing of up-sampled model and atomic norm for super-resolution radar",
abstract = "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.",
author = "Dongshin Yang and Yutaka Jitsumatsu",
year = "2017",
month = "8",
day = "10",
doi = "10.23919/IRS.2017.8008110",
language = "English",
series = "Proceedings International Radar Symposium",
publisher = "IEEE Computer Society",
editor = "Hermann Rohling",
booktitle = "2017 18th International Radar Symposium, IRS 2017",
address = "United States",

}

TY - GEN

T1 - Compressive sensing of up-sampled model and atomic norm for super-resolution radar

AU - Yang, Dongshin

AU - Jitsumatsu, Yutaka

PY - 2017/8/10

Y1 - 2017/8/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85028624773&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028624773&partnerID=8YFLogxK

U2 - 10.23919/IRS.2017.8008110

DO - 10.23919/IRS.2017.8008110

M3 - Conference contribution

T3 - Proceedings International Radar Symposium

BT - 2017 18th International Radar Symposium, IRS 2017

A2 - Rohling, Hermann

PB - IEEE Computer Society

ER -