Sparse source location for real aperture radar using generalized sparse covariance fitting

Yongchao Zhang; Yin Zhang; Yulin Huang; Jianyu Yang; Andreas Jakobsson

doi:10.1109/RADAR.2017.7944363

Sparse source location for real aperture radar using generalized sparse covariance fitting

Yongchao Zhang, Yin Zhang, Yulin Huang, Jianyu Yang, Andreas Jakobsson

Mathematical Statistics

University of Electronic Science and Technology of China

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.

Original language	English
Title of host publication	2017 IEEE Radar Conference, RadarConf 2017
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	1069-1074
Number of pages	6
ISBN (Electronic)	9781467388238
DOIs	https://doi.org/10.1109/RADAR.2017.7944363
Publication status	Published - 2017 Jun 7
Event	2017 IEEE Radar Conference, RadarConf 2017 - Seattle, United States Duration: 2017 May 8 → 2017 May 12

Conference

Conference	2017 IEEE Radar Conference, RadarConf 2017
Country/Territory	United States
City	Seattle
Period	2017/05/08 → 2017/05/12

Subject classification (UKÄ)

Electrical Engineering, Electronic Engineering, Information Engineering

Access to Document

10.1109/RADAR.2017.7944363

Cite this

@inproceedings{8266976bbc374b81825e86398a3a8628,

title = "Sparse source location for real aperture radar using generalized sparse covariance fitting",

abstract = "Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.",

author = "Yongchao Zhang and Yin Zhang and Yulin Huang and Jianyu Yang and Andreas Jakobsson",

year = "2017",

month = jun,

day = "7",

doi = "10.1109/RADAR.2017.7944363",

language = "English",

pages = "1069--1074",

booktitle = "2017 IEEE Radar Conference, RadarConf 2017",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE Radar Conference, RadarConf 2017 ; Conference date: 08-05-2017 Through 12-05-2017",

}

Zhang, Y, Zhang, Y, Huang, Y, Yang, J & Jakobsson, A 2017, Sparse source location for real aperture radar using generalized sparse covariance fitting. in 2017 IEEE Radar Conference, RadarConf 2017., 7944363, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 1069-1074, 2017 IEEE Radar Conference, RadarConf 2017, Seattle, United States, 2017/05/08. https://doi.org/10.1109/RADAR.2017.7944363

Sparse source location for real aperture radar using generalized sparse covariance fitting. / Zhang, Yongchao; Zhang, Yin; Huang, Yulin et al.
2017 IEEE Radar Conference, RadarConf 2017. IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. p. 1069-1074 7944363.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Sparse source location for real aperture radar using generalized sparse covariance fitting

AU - Zhang, Yongchao

AU - Zhang, Yin

AU - Huang, Yulin

AU - Yang, Jianyu

AU - Jakobsson, Andreas

PY - 2017/6/7

Y1 - 2017/6/7

N2 - Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.

AB - Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.

U2 - 10.1109/RADAR.2017.7944363

DO - 10.1109/RADAR.2017.7944363

M3 - Paper in conference proceeding

AN - SCOPUS:85021394516

SP - 1069

EP - 1074

BT - 2017 IEEE Radar Conference, RadarConf 2017

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Radar Conference, RadarConf 2017

Y2 - 8 May 2017 through 12 May 2017

ER -

Sparse source location for real aperture radar using generalized sparse covariance fitting

Abstract

Conference

Subject classification (UKÄ)

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