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

Yifei LIU  Yuan ZHAO  Jun ZHU  Bin TANG  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.3   pp.581-585
Publication Date: 2019/03/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.581
Type of Manuscript: LETTER
Category: Digital Signal Processing
Keyword: 
passive localization,  Nyquist folding receiver,  spaceborne synthetic aperture radar,  

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Summary: 
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.