Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting

Zhang, Yongchao ; Mao, Deqing ; Bu, Yuanyuan ; Wu, Junjie ; Huang, Yulin and Jakobsson, Andreas LU orcid (2019) IGARSS 2019 p.8562-8565
Abstract
Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The... (More)
Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium - 2019 IEEE International Geoscience and Remote Sensing Symposium
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IGARSS 2019
conference location
Yokohama, Japan
conference dates
2019-07-28 - 2019-08-02
external identifiers
  • scopus:85077689546
ISBN
978-1-5386-9155-7
978-1-5386-9154-0
DOI
10.1109/IGARSS.2019.8899156
language
English
LU publication?
yes
id
b4b95192-cd47-4d04-a5f1-33a6ca2a157c
date added to LUP
2019-05-31 15:35:32
date last changed
2024-06-25 17:08:13
@inproceedings{b4b95192-cd47-4d04-a5f1-33a6ca2a157c,
  abstract     = {{Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.}},
  author       = {{Zhang, Yongchao and Mao, Deqing and Bu, Yuanyuan and Wu, Junjie and Huang, Yulin and Jakobsson, Andreas}},
  booktitle    = {{IGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium}},
  isbn         = {{978-1-5386-9155-7}},
  language     = {{eng}},
  month        = {{11}},
  pages        = {{8562--8565}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting}},
  url          = {{http://dx.doi.org/10.1109/IGARSS.2019.8899156}},
  doi          = {{10.1109/IGARSS.2019.8899156}},
  year         = {{2019}},
}