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

Zhang, Yongchao; Zhang, Yin; Huang, Yulin; Yang, Jianyu and Jakobsson, Andreas LU (2017) 2017 IEEE Radar Conference, RadarConf 2017 In 2017 IEEE Radar Conference, RadarConf 2017 p.1069-1074
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.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2017 IEEE Radar Conference, RadarConf 2017
pages
6 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
2017 IEEE Radar Conference, RadarConf 2017
external identifiers
  • scopus:85021394516
ISBN
9781467388238
DOI
10.1109/RADAR.2017.7944363
language
English
LU publication?
yes
id
8266976b-bc37-4b81-825e-86398a3a8628
date added to LUP
2017-07-12 10:21:19
date last changed
2018-01-07 12:11:20
@inproceedings{8266976b-bc37-4b81-825e-86398a3a8628,
  abstract     = {<p>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.</p>},
  author       = {Zhang, Yongchao and Zhang, Yin and Huang, Yulin and Yang, Jianyu and Jakobsson, Andreas},
  booktitle    = {2017 IEEE Radar Conference, RadarConf 2017},
  isbn         = {9781467388238},
  language     = {eng},
  month        = {06},
  pages        = {1069--1074},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Sparse source location for real aperture radar using generalized sparse covariance fitting},
  url          = {http://dx.doi.org/10.1109/RADAR.2017.7944363},
  year         = {2017},
}