Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging

Huang, Duan; Zhang, Lizao; Fang, Jun; Li, Hongbin

doi:10.1109/lsp.2015.2452412

Cited by 76 publications

(32 citation statements)

References 13 publications

(27 reference statements)

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“…3. 5 show the ISAR images using PC-SBL (the comparsion between PC-SBL and other methods can be refered in [10]) and BL1L0 algorithm with pulses of 16 and 32. The Matlab program of PC-SBL is down loaded from http://www.junfang-uestc.net/codes/ISAR.rar.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For the PC-SBL algorithm, the parameters used are the default parameters in the downloaded program, where β = 1, four contiguous range profiles are combined as the data of a MMV equation. Even though the original PC-SBL algorithm can process more contiguous range profiles at one time, due to the huge coefficient matrix formed, [10] used four contiguous range profiles to save memories and computations. For the BL1L0 algorithm, as in [3], the noise level is estimated from the range profiles, that is, using range cells from 1 to 25 to estimate the noise variance.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The scatterers of man made targets are usually clusters or blocks. In order to use the block property of the scatterers, block sparse Bayesian learning based on Markov random fields and pattern-coupled sparse Bayesian learning (PC-SBL) algorithms are proposed [8][9][10]. However, these two methods are computational expensive, especially of [9], where forming a 128 × 64 ISAR image needs over one hour using a prevalent personal computer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Isar Imaging Based on L1 L0 Norms Homotopy 2d Block Sparse Signal Recovery Algorithm

Ma¹,

Ng²,

Feng³

2016

PIER C

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Abstract-Many traditional sparse signal recovery based ISAR imaging methods did not utilize the block scatterers information of targets. Some block Bayesian learning based ISAR imaging algorithms are computational expensive. In this paper, a 2D block 1 0 norms homotopy sparse signal recovery algorithm (the BL1L0 algorithm) is proposed and utilized to form the ISAR image. Compared with Bayesian-based algorithms, this algorithm can obtain ISAR images with similar image quality, but the computation speed is faster. Real data experiments verify the merits of our algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Isar Imaging Based on L1 L0 Norms Homotopy 2d Block Sparse Signal Recovery Algorithm

Ma¹,

Ng²,

Feng³

2016

PIER C

View full text Add to dashboard Cite

show abstract

“…In following years, the model-based ISAR imaging have been studied intensively for different application scenarios, for example, the 3D ISAR [58] imaging using 2D sparse array [188], the stepped-frequency-waveform ISAR imaging [186], the passive ISAR imaging [133], and so on. In this research branch, the study at its early stage is focused on the image scene that is sparse by itself.…”

Section: Isar Imagingmentioning

confidence: 99%

A Survey on the Low-Dimensional-Model-based Electromagnetic Imaging

Hurtado

et al. 2018

FNT in Signal Processing

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The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many

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“…Conventional monostatic ISAR images are obtained under the condition that the change of the radar observation angle is small (<

) [ 1 ]. For modern high maneuvering aircraft with high speed, stealth and other characteristics, even a slight change of the observation angle can cause a fluctuation of 10 to 15 dB on the RCS [ 2 ]. The strong fluctuation of the RCS will cause a deterioration in the ISAR image quality.…”

Section: Introductionmentioning

confidence: 99%

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform

Zhang

2018

Sensors

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In real applications, the image quality of the conventional monostatic Inverse Synthetic Aperture Radar (ISAR) for the maneuvering target is subject to the strong fluctuation of Radar Cross Section (RCS), as the target aspect varies enormously. Meanwhile, the maneuvering target introduces nonuniform rotation after translation motion compensation which degrades the imaging performance of the conventional Fourier Transform (FT)-based method in the cross-range dimension. In this paper, a method which combines the distributed ISAR technique and the Matching Fourier Transform (MFT) is proposed to overcome these problems. Firstly, according to the characteristics of the distributed ISAR, the multiple channel echoes of the nonuniform rotation target from different observation angles can be acquired. Then, by applying the MFT to the echo of each channel, the defocused problem of nonuniform rotation target which is inevitable by using the FT-based imaging method can be avoided. Finally, after preprocessing, scaling and rotation of all subimages, the noncoherent fusion image containing all the RCS information in all channels can be obtained. The accumulation coefficients of all subimages are calculated adaptively according to the their image qualities. Simulation and experimental data are used to validate the effectiveness of the proposed approach, and fusion image with improved recognizability can be obtained. Therefore, by using the distributed ISAR technique and MFT, subimages of high-maneuvering target from different observation angles can be obtained. Meanwhile, by employing the adaptive subimage fusion method, the RCS fluctuation can be alleviated and more recognizable final image can be obtained.

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Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging

Cited by 76 publications

References 13 publications

Isar Imaging Based on L1 L0 Norms Homotopy 2d Block Sparse Signal Recovery Algorithm

Isar Imaging Based on L1 L0 Norms Homotopy 2d Block Sparse Signal Recovery Algorithm

A Survey on the Low-Dimensional-Model-based Electromagnetic Imaging

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform

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