Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting

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Standard

Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. / Zhang, Yongchao; Mao, Deqing; Bu, Yuanyuan; Wu, Junjie; Huang, Yulin; Jakobsson, Andreas.

IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan : IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. s. 8562-8565.

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Harvard

Zhang, Y, Mao, D, Bu, Y, Wu, J, Huang, Y & Jakobsson, A 2019, Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. i IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE - Institute of Electrical and Electronics Engineers Inc., Yokohama, Japan, s. 8562-8565, IGARSS 2019 , Yokohama, Japan, 2019/07/28. https://doi.org/10.1109/IGARSS.2019.8899156

APA

Zhang, Y., Mao, D., Bu, Y., Wu, J., Huang, Y., & Jakobsson, A. (2019). Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. I IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium (s. 8562-8565). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8899156

CBE

Zhang Y, Mao D, Bu Y, Wu J, Huang Y, Jakobsson A. 2019. Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. I IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE - Institute of Electrical and Electronics Engineers Inc. s. 8562-8565. https://doi.org/10.1109/IGARSS.2019.8899156

MLA

Zhang, Yongchao et al. "Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting". IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE - Institute of Electrical and Electronics Engineers Inc. 2019, 8562-8565. https://doi.org/10.1109/IGARSS.2019.8899156

Vancouver

Zhang Y, Mao D, Bu Y, Wu J, Huang Y, Jakobsson A. Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. I IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE - Institute of Electrical and Electronics Engineers Inc. 2019. s. 8562-8565 https://doi.org/10.1109/IGARSS.2019.8899156

Author

Zhang, Yongchao ; Mao, Deqing ; Bu, Yuanyuan ; Wu, Junjie ; Huang, Yulin ; Jakobsson, Andreas. / Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting. IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan : IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. s. 8562-8565

RIS

TY - GEN

T1 - Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting

AU - Zhang, Yongchao

AU - Mao, Deqing

AU - Bu, Yuanyuan

AU - Wu, Junjie

AU - Huang, Yulin

AU - Jakobsson, Andreas

PY - 2019/11/14

Y1 - 2019/11/14

N2 - 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.

AB - 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.

UR - https://scopus.com/record/display.uri?eid=2-s2.0-85077689546&origin=inward&txGid

U2 - 10.1109/IGARSS.2019.8899156

DO - 10.1109/IGARSS.2019.8899156

M3 - Paper in conference proceeding

SN - 978-1-5386-9155-7

SP - 8562

EP - 8565

BT - IGARSS 2019

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

CY - Yokohama, Japan

T2 - IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -