MCA: A Multichannel Approach to SAR Autofocus

Morrison, Rick L.; Minh, N.; Munson, D.C.

doi:10.1109/tip.2009.2012883

Cited by 58 publications

(45 citation statements)

References 23 publications

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“…However, when we consider the unknown noise ν = 0 and the estimationf n+1 is not the exact solution, the direct calculation method may fail. Here, we solve Equation (27) by maximum likelihood estimation 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%

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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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Section: The Sparse Autofocus Recovery Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wei¹

2013

PIER

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“…Blind gain and phase calibration (BGPC), the joint recovery of the unknown gains and phases in the sensing system and the unknown signal, is a bilinear inverse problem that arises in many applications: the joint estimation of albedo and lighting conditions in inverse rendering [2]; the joint recovery of phase error and radar image in synthetic aperture radar (SAR) autofocus [3]; and auto-calibration of sensor gains and phases in This work was supported in part by the National Science Foundation under Grant IIS 14-47879. This paper was presented in part at SampTA 2017 [1].…”

Section: Introductionmentioning

confidence: 99%

Blind Gain and Phase Calibration via Sparse Spectral Methods

Lee

Bresler

2019

IEEE Trans. Inform. Theory

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Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the ill-posedness of BGPC. Recently there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem, and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.

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“…It is well known that these phase errors come mainly from two sources, [1], [2], [3]. A low frequency phase error will exist for uncompensated platform deviation, which has the effect of broadening the main-lobe of the azimuth compressed signals.…”

Section: Introductionmentioning

confidence: 99%

Convex optimization of subspace fitting autofocus for stripmap SAR images

West

Gunther

Moon

2012

2012 IEEE Radar Conference

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There are many different autofocus algorithms for correcting phase errors in stripmap SAR images. In this paper, we develop a mathematically principled model-based phase error estimation method for stripmap SAR images. The proposed phase estimation method uses classical subspace fitting techniques that are well known in the array processing literature. The novelty in this paper is the development of the proposed autofocus method and how the phase estimates are found using convex optimization. It is shown that the proposed method is non-iterative in the sense that iterations between the image domain and the range compressed domain to obtain our phase error estimates are not needed. It is also shown that the phase error estimates can be applied while simultaneously forming the SAR image using the convolution back-projection algorithm. I. BACKGROUNDDespite the best efforts to have a synthetic aperture radar (SAR) sensor follow a predetermined nominal flight path, phase errors still exist in the SAR data that corrupt the focused image. It is well known that these phase errors come mainly from two sources, [1], [2], [3]. A low frequency phase error will exist for uncompensated platform deviation, which has the effect of broadening the main-lobe of the azimuth compressed signals. The other source of phase error stems from signal propagation effects. These phase errors tend to be high frequency phase errors and the effect is not so much a broadening of the main lobe of the azimuth compressed pulse as raising the side-lobes, which lowers the contrast of the image and masks less reflective objects. Autofocus algorithms are data driven algorithms that estimate and correct these phase errors.There are several autofocus algorithms for stripmap SAR that can be classified as parametric, non-parametric, or metric based. The parametric approaches tend to model the phase error as a polynomial or as sinusoidal and use the data to estimate the coefficients, [2], [3], [4], [5]. Since the data is used to estimate the coefficients, these methods are limited to only reliably estimating a small number of coefficients. These approaches perform well if the phase error is low frequency. The non-parametric approaches make the assumption that all of the range data collected from a single pulse is corrupted by the same phase error, [2], [6], [7]. If this holds, nonparametric techniques are able to estimate both low and high frequency phase errors. Finally, the metric based methods generally assume that the phase error is low frequency and that it comes from a sensor velocity error. However, these methods can also be combined with non-parametric methods to produce good results, [8], [9], [10]. They utilize a cost function, such as image intensity or neg-entropy, to correct the azimuth matched filter that is applied to azimuth compress the data.What most of these methods have in common is that they must step back in the processing chain (usually azimuth decompression) to where the individual phase errors from each pulse exist to apply the phase...

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MCA: A Multichannel Approach to SAR Autofocus

Cited by 58 publications

References 23 publications

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

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

Blind Gain and Phase Calibration via Sparse Spectral Methods

Convex optimization of subspace fitting autofocus for stripmap SAR images

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