Signal Processing for Microwave Array Imaging: TDC and Sparse Recovery

Shi, Jun; Gao, Xiang; Jianyu, Jianyu

doi:10.1109/tgrs.2012.2191293

Cited by 25 publications

(3 citation statements)

References 36 publications

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“…Step (5) reconstructs the whole frequency response of sampled pixels, these reconstruction results fill the unsampled frames in spatial domain, which works as a virtual measurement process in step (6). All frames have sampled data and these data are in the same location for each frame due to the aligned property.…”

Section: Aligned Spatial-spectral Sensingmentioning

confidence: 99%

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Feature-Supervised Compressed Sensing for Microwave Imaging Systems

Tang

Tian

Boussakta

et al. 2020

IEEE Trans. Instrum. Meas.

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Section: Aligned Spatial-spectral Sensingmentioning

confidence: 99%

“…To reduce sampling time, some designs use more hardware such as sensor arrays [6] to parallelly sample multiple spatial pixels. But these designs increase hardware cost.…”

mentioning

confidence: 99%

Feature-Supervised Compressed Sensing for Microwave Imaging Systems

Tang

Tian

Boussakta

et al. 2020

IEEE Trans. Instrum. Meas.

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“…In this framework, sparsity promoting regularized approaches assume that the unknown functions can be expressed as a sparse set of coefficients with respect to an appropriate basis. In recent years, sparse methods for microwave imaging have been developed, see [10], [11], using a linear approximation of the scattered signal. One of the main contributions of the proposed paper is that it is based on joint sparsity.…”

Section: Introductionmentioning

confidence: 99%

Sparse reconstruction algorithms for nonlinear microwave imaging

Zaimaga¹,

Fraysse²,

Lambert³

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a two-step inverse process which allows sparse recovery of the unknown (complex) dielectric profiles of scatterers for nonlinear microwave imaging. The proposed approach is applied to a nonlinear inverse scattering problem arising in microwave imaging and correlated with joint sparsity which gives multiple sparse solutions that share a common nonzero support. Numerical results demonstrate the potential of the proposed two step inversion approach when compared to existing sparse recovery algorithm for the case of small scatterers.

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DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

Bao

Han

Peng³

et al. 2016

Sci. China Inf. Sci.

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This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.

show abstract

Signal Processing for Microwave Array Imaging: TDC and Sparse Recovery

Cited by 25 publications

References 36 publications

Feature-Supervised Compressed Sensing for Microwave Imaging Systems

Feature-Supervised Compressed Sensing for Microwave Imaging Systems

Sparse reconstruction algorithms for nonlinear microwave imaging

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

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