Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

Peng, Xueming; Wang, Yanping; Hong, Wen; Tan, Weixian; Wu, Yirong

doi:10.3390/rs5105304

Cited by 11 publications

(19 citation statements)

References 34 publications

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“…According to the system parameters we know that the cross-track Rayleigh resolution [5] ρ c = λR ( m,n ; P )/(2 L c ) is about 1.6 m, and the cross-track reconstruction pixel grid is chosen to match the Rayleigh resolution. Considering the spatial sparsity of a 3-D scene, we assume that ten scatterers, which have the same wave-propagation and along-track locations, randomly distribute on different cross-track pixel grids in the effective imaging region [3]. The on-grid scatterers are all with unit magnitudes and uniform random phases in the range (–π,π).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Thus, the 3-D regions of interested behave typically spatial sparsity, i.e., each wave-propagation and along-track pixel contains only a limited number of dominating scatterers compared with the total cross-track dimension [3,4,5]. Therefore, the cross-track imaging of DLSLA 3-D SAR can be regarded as the problem of sparse signal reconstruction.…”

Section: Dlsla 3-d Sar Signal Model and Sparse Reconstruction Condmentioning

confidence: 99%

“…With linear array antennas distributed along the wing of a rectilinearly moving platform and a downward-looking imaging geometry, DLSLA 3-D SAR can synthesize a 2-D plane array and obtain the 3-D resolution of the imaging scene [1,2,3,4,5,6]. …”

Section: Introductionmentioning

confidence: 99%

“…The former is caused by the length limitation of the array, which is restricted by the platform wingspan. The latter is mainly rendered by the sparse and non-uniform distribution of the APCs, due to the installation restriction, burden of expense and so on [3,4,5]. Considering the spatial sparsity of a 3-D imaging scene, compressive sensing (CS) [7] provides a solution for super-resolution.…”

Section: Introductionmentioning

confidence: 99%

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

Bao

Jiang

Lin

et al. 2016

Sensors

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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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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Dlsla 3-d Sar Signal Model and Sparse Reconstruction Condmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bao

Jiang

Lin

et al. 2016

Sensors

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“…Until now, two LA-3D-SAR systems have been developed, 9,10 and some 3-D image formation algorithms have been proposed. [11][12][13][14][15][16] As a multichannel coherent system, LA-3D-SAR is particularly susceptible to channel inconsistency, which will cause geometric distortion, loss of resolution, decrease in image contrast, spurious targets, and a reduction in signal-to-noise ratio (SNR). 17 However, under realistic operational conditions, the antenna elements are subject to array errors such as multichannel amplitude-phase errors and antenna position errors.…”

Section: Introductionmentioning

confidence: 99%

Array error calibration methods in downward-looking linear-array three-dimensional synthetic aperture radar

Tan

Huang

Han

et al. 2016

J. Appl. Remote Sens

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, "Array error calibration methods in downward-looking linear-array three-dimensional synthetic aperture radar," J. Appl. Remote Sens. 10(2), 025010 (2016), doi: 10.1117/1.JRS.10.025010. Abstract. In order to achieve high-precision three-dimensional (3-D) imaging with an airborne downward-looking linear-array 3-D synthetic aperture radar (LA-3D-SAR), a uniform virtual antenna array can be obtained by aperture synthesis of the cross-track sparse multiple-inputmultiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by array errors such as the multichannel amplitude-phase errors due to the nonideal antenna characteristics, and the virtual element position errors due to vibrations and motion measurement deviations. We investigate the effects of these errors on the forms and the degrees of image quality degradation and consider the use of corresponding calibration methods to eliminate the effects of errors. For the multichannel amplitude-phase errors, the target response is subject to an integrated sidelobe level increase introduced by the phase error, which can be calibrated based on external (parallel or point target) calibrators, as proposed in the paper. For the virtual element position errors, they mainly the result of contrast degradation and noise in the image along the cross-track direction and have little impact on the range and along-track directions. The imaging performance is more sensitive to the error component in the height direction as compared to other components, the precision requirement of which should be established as the calibration reference. A calibration method based on time-divided active calibrators is proposed to estimate and correct the virtual element position errors. Both numerical simulations and real data experiments have shown the validity of the analyses as well as the effectiveness of the proposed calibration methods.

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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.

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Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

Cited by 11 publications

References 34 publications

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

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

Array error calibration methods in downward-looking linear-array three-dimensional synthetic aperture radar

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

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