Title: Trench-Zohar inversion for SAR sensor network 3-D imaging based on compressive sensing

Authors: Rui Min; Ruizhi Hu; Qianqian Yang

Addresses: School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China ' School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China ' School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract: Recently, Compressive Sensing has become a highly attractive technique for SAR imaging since it outperforms existing methods. In this paper, a Sparse Bayesian Learning based approach in Compressive Sensing framework is proposed for SAR sensor network imaging to reduce the required number of sensors and to obtain super-resolution in the elevation direction. Specifically, the Trench-Zohar inversion is also adapted to the normal Sparse Bayesian Learning algorithm to reduce the computation time and storage requirements. The advanced efficiency of the proposed approach is validated by results achieved from different simulations.

Keywords: compressive sensing; 3D imaging; synthetic aperture radar; SAR sensor networks; Trench-Zohar inversion; sparse Bayesian learning; simulation.

DOI: 10.1504/IJSNET.2013.055584

International Journal of Sensor Networks, 2013 Vol.13 No.4, pp.219 - 224

Published online: 30 Jul 2013 *

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