Synthetic Aperture Radar Autofocus Based on a Bilinear Model

Liu, Kuang-Hung; Wiesel, Ami; Munson, D.C.

doi:10.1109/tip.2012.2183881

Cited by 28 publications

(10 citation statements)

References 23 publications

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“…Assume that the phase errors have been compensated by autofocus algorithms, e.g. [12]. After range compression, the echoed signal is given as (2) where denotes the scattering amplitude, is the slow time, is the coherent processing interval, is the sinc function, denotes the speed of light, is the wavelength, and is the instantaneous distance between the scatterer and the radar which can be approximated by , where is the target coordinate origin, and denotes the rotational angular velocity (see Fig.…”

Section: Isar Imaging Modelmentioning

confidence: 99%

“…Clearly, we have , , and . Based on the above hierarchical model, the posterior distribution of can be computed as (12) According to (7), it can be readily verified that the posterior follows a Gaussian distribution with its mean and covariance matrix given respectively by (13) where is a diagonal matrix with its th diagonal element equal to , i.e.…”

Section: Bayesian Inferencementioning

confidence: 99%

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

Huang

Zhang

Fang

et al. 2015

IEEE Signal Process. Lett.

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We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.Index Terms-Block-sparse structure, expectation-maximization (EM), ISAR, pattern-coupled sparse bayesian learning.1070-9908

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Section: Isar Imaging Modelmentioning

confidence: 99%

Section: Bayesian Inferencementioning

confidence: 99%

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

Huang

Zhang

Fang

et al. 2015

IEEE Signal Process. Lett.

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“…, the problem of Equation (28) can be formulated as a positive semi-definite matrices optimization program aŝ…”

Section: The Sparse Autofocus Recovery Approachmentioning

confidence: 99%

“…Nevertheless, phase errors are seldom taken into account for most existing CS-based SAR imaging. Although various autofocus algorithms have been presented for SAR phase errors correction, e.g., phase gradient autofocus (PGA) algorithm [26], multichannel autofocus (MCA) algorithm [27] and maximum likelihood autofocus (MLA) algorithm [28], etc., most of them are based on post-processing of the conventional fully-samples SAR image. In the case of SA-SAR, as the LAA is not full-sampled, these classical autofocus algorithms may suffer from significant performance loss.…”

Section: Introductionmentioning

confidence: 99%

Sparse Autofocus Recovery for Under-Sampled Linear Array Sar 3-D Imaging

Wei¹

2013

PIER

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Abstract-Linear array synthetic aperture radar (LASAR) is a promising radar 3-D imaging technique. In this paper, we address the problem of sparse recovery of LASAR image from under-sampled and phase errors interrupted echo data. It is shown that the unknown LASAR image and the nuisance phase errors can be constructed as a bilinear measurement model, and then the under-sampled LASAR imaging with phase errors can be mathematically transferred into sparse signal recovery by solving an ill-conditioned constant modulus linear program (ICCMLP) problem. Exploiting the prior sparse spatial feature of the observed targets, a new super-resolution sparse autofocus recovery algorithm is proposed for under-sampled LASAR 3-D imaging. The algorithm is an iterative minimize estimation procedure, wherein it converts the ICCMLP into two independent convex optimal problems, and joints 1 -norm reweights least square regularization and semi-definite relax technique to find the optimal solutions. Simulated and experimental results confirm that the proposed method outperforms the classical autofocus techniques in under-sampled LASAR imaging.

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“…Method [12], and Maximum-likelihood Autofocus (MLA) [13]. As the SAR resolution becomes finer, however, the increased size of synthetic aperture makes the accumulated range error more pronounced.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery

Mao

Zhu

2016

IEEE Trans. Comput. Imaging

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Conventional two-dimensional (2-D) autofocus algorithms blindly estimate the phase error in the sense that they do not exploit any a priori information on the structure of the 2-D phase error. As such, they often suffer from low computational efficiency and lack of data redundancy to accurately estimate the 2-D phase error. In this paper, a knowledge-aided (KA) 2-D autofocus algorithm which is based on exploiting a priori knowledge about the 2-D phase error structure, is presented. First, as a prerequisite of the proposed KA method, the analytical structure of residual 2-D phase error in SAR imagery is investigated in the polar format algorithm (PFA) framework. Then, by incorporating this a priori information, a novel 2-D autofocus approach is proposed. The new method only requires an estimate of azimuth phase error and/or residual range cell migration, while the 2-D phase error can then be computed directly from the estimated azimuth phase error or residual range cell migration. This 2-D autofocus method can also be applied to refocus moving targets in PFA imagery. Experimental results clearly demonstrate the effectiveness and robustness of the proposed method.

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Synthetic Aperture Radar Autofocus Based on a Bilinear Model

Cited by 28 publications

References 23 publications

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

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

Sparse Autofocus Recovery for Under-Sampled Linear Array Sar 3-D Imaging

Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery

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