ArticleSensors (Basel, Switzerland)2016
Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems.Sensors (Basel, Switzerland) · 2018Article
- Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference.Sensors (Basel, Switzerland) · 2018Article
- Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array.Sensors (Basel, Switzerland) · 2018Article
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6 authors.
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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.
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