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A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.3 pp.581-585

- Publication Date
- 2019/03/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E102.A.581

- Type of Manuscript
- LETTER

- Category
- Digital Signal Processing

Yifei LIU

University of Electronic Science and Technology of China (UESTC)

Yuan ZHAO

University of Electronic Science and Technology of China (UESTC)

Jun ZHU

University of Electronic Science and Technology of China (UESTC)

Bin TANG

University of Electronic Science and Technology of China (UESTC)

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Yifei LIU, Yuan ZHAO, Jun ZHU, Bin TANG, "Passive Localization Algorithm for Spaceborne SAR Using NYFR and Sparse Bayesian Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 3, pp. 581-585, March 2019, doi: 10.1587/transfun.E102.A.581.

Abstract: A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.581/_p

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@ARTICLE{e102-a_3_581,

author={Yifei LIU, Yuan ZHAO, Jun ZHU, Bin TANG, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Passive Localization Algorithm for Spaceborne SAR Using NYFR and Sparse Bayesian Learning},

year={2019},

volume={E102-A},

number={3},

pages={581-585},

abstract={A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.},

keywords={},

doi={10.1587/transfun.E102.A.581},

ISSN={1745-1337},

month={March},}

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TY - JOUR

TI - Passive Localization Algorithm for Spaceborne SAR Using NYFR and Sparse Bayesian Learning

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 581

EP - 585

AU - Yifei LIU

AU - Yuan ZHAO

AU - Jun ZHU

AU - Bin TANG

PY - 2019

DO - 10.1587/transfun.E102.A.581

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E102-A

IS - 3

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - March 2019

AB - A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.

ER -