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Sensors 2016, 16(8), 1333; doi:10.3390/s16081333

Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction

1
Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences (IECAS), Beijing 100190, China
2
University of Chinese Academy of Sciences (UCAS), Beijing 100190, China
3
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia, China
4
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: Daniele Riccio
Received: 6 April 2016 / Revised: 6 August 2016 / Accepted: 6 August 2016 / Published: 22 August 2016
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Abstract

With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of measurement matrix. This paper concerns the technique to optimize the measurement matrix and deal with the mismatch problem of measurement matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the measurement matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the measurement matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method. View Full-Text
Keywords: DLSLA 3-D SAR; measurement matrix optimization; mutual coherence; measurement matrix mismatch; sparse Bayesian inference DLSLA 3-D SAR; measurement matrix optimization; mutual coherence; measurement matrix mismatch; sparse Bayesian inference
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MDPI and ACS Style

Bao, Q.; Jiang, C.; Lin, Y.; Tan, W.; Wang, Z.; Hong, W. Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction. Sensors 2016, 16, 1333.

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