Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting
(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:
https://lup.lub.lu.se/record/b4b95192-cd47-4d04-a5f1-33a6ca2a157c
- author
- Zhang, Yongchao ; Mao, Deqing ; Bu, Yuanyuan ; Wu, Junjie ; Huang, Yulin and Jakobsson, Andreas LU
- organization
- publishing date
- 2019-11-14
- 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
- 978-1-5386-9154-0
- 978-1-5386-9155-7
- 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 = {{978-1-5386-9154-0}}, 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}}, }