Sparsity-driven image formation and space-variant focusing for SAR

In synthetic aperture radar (SAR) imaging, the presence of moving targets in the scene causes phase errors in the SAR data and subsequently defocusing in the formed image. The defocusing caused by the moving targets exhibits space-variant characteristics, i.e., the defocusing arises only in the parts of the image containing the moving targets, whereas the stationary background is not defocused. Considering that the reflectivity field to be imaged usually admits sparse representation, we propose a sparsity-driven method for joint SAR imaging and removing the defocus caused by moving targets. The method is performed in a nonquadratic regular-ization based framework by solving an optimization problem, in which prior information about both the scene and phase errors are incorporated as constraints

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

SAR moving target imaging using group sparsity

SAR imaging of scenes containing moving targets results in defocusing in the reconstructed images if the SAR observation model used in imaging does not take the motion into account. SAR data from a scene with motion can be viewed as data from a stationary scene, but with phase errors due to motion. Based on this perspective, we formulate the moving target SAR imaging problem as one of joint imaging and phase error compensation. Based on the assumption that only a small percentage of the entire scene contains moving targets, phase errors exhibit a group sparse nature, when the entire data for all the points in the scene are handled together. Considering this structure of motion-related phase errors and that many scenes of interest admit sparse representation in SAR imaging, we solve this joint problem by minimizing a cost function which involves two nonquadratic regularization terms one of which is used to enforce the sparsity of the reflectivity field to be imaged and the other is used to exploit the group sparse nature of the phase errors

SAR moving target imaging in a sparsity-driven framework

In synthetic aperture radar (SAR) imaging, sparsity-driven imaging techniques have been shown to provide high resolution images with reduced sidelobes and reduced speckle, by allowing the incorporation of prior information about the scene into the problem. Just like many common SAR imaging methods, these techniques also assume the targets in the scene are stationary over the data collection interval. Here, we consider the problem of imaging in the presence of targets with unknown motion in the scene. Moving targets cause phase errors in the SAR data and these errors lead to defocusing in the corresponding spatial region in the reconstructed image. We view phase errors resulting from target motion as errors on the observation model of a static scene. Based on these observations we propose a method which not only benefits from the advantages of sparsity-driven imaging but also compansates the errors arising due to the moving targets. Considering that in SAR imaging the underlying scene usually admits a sparse representation, a nonquadratic regularization-based framework is used. The proposed method is based on minimization of a cost function which involves regularization terms imposing sparsity on the reflectivity field to be imaged, as well as on the spatial structure of the motion-related phase errors, reflecting the assumption that only a small percentage of the entire scene contains moving targets. Experimental results demonstrate the effectiveness of the proposed approach in reconstructing focused images of scenes containing multiple targets with unknown motion

Faz hatalı SAR verileri için karesel olmayan düzenlileştirmeye dayalı bir görüntü oluşturma tekniği (A nonquadratic regularization based image reconstruction technique for SAR data with phase errors)

Sentetik Açıklıklı Radar (SAR) görüntülemesinde karşılaşılan önemli problemlerden biri faz hatalarıdır. Faz hataları, SAR’ın bulunduğu platformla hedef arasındaki uzaklığın tam olarak ölçülememesi ya da gönderilen işaretlerin atmosferdeki türbülansa bağlı olarak rasgele gecikmelere uğraması nedeniyle, SAR tarafından gönderilen sinyallerin hedefe gidip geri gelmesi için gereken zamanın tam olarak belirlenememesinden kaynaklanır ve oluşturulan SAR imgesinde çapraz menzil yönünde bulanıklaşmaya neden olurlar. Bu çalışmada, karesel olmayan düzenlileştirmeye dayalı bir çerçevede aynı anda hem görüntüleme hem de faz hatası kestirimi ile düzeltmesi yapan bir yöntem önerilmektedir. Yöntem, hem görüntüye hem de faz hatasına bağlı bir amaç fonksiyonunun eniyilenmesine dayanmaktadır. Deney sonuçları, önerilen yöntemin etkinliğini göstermektedir. --- One of the fundamental problems in Synthetic Aperture Radar (SAR) imaging is phase errors. Phase errors occur when the time required for the transmitted signal from SAR to the target and back cannot be obtained properly either because the distance between the SAR platform and the target cannot be measured exactly or in the case of random delays in the signal due to propagation in atmospheric turbulence. Phase errors cause blurring of the reconstructed image in the cross range direction. In this study, a nonquadratic regularization-based framework is proposed for joint image formation and phase error removal. The method involves the optimization of a cost function with respect to the image as well as the phase errors. Experimental results show the effectiveness of the proposed method

A nonquadratic regularization-based technique for joint SAR imaging and model error correction

Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known. However, in practice, it is very common to encounter various types of model errors. One predominant example is phase errors which appear either due to inexact measurement of the location of the SAR sensing platform, or due to effects of propagation through atmospheric turbulence. We propose a nonquadratic regularization-based framework for joint image formation and model error correction. This framework leads to an iterative algorithm, which cycles through steps of image formation and model parameter estimation. This approach offers advantages over autofocus techniques that involve post-processing of a conventionally formed image. We present results on synthetic scenes, as well as the Air Force Research Laboratory (AFRL) Backhoe data set, demonstrating the effectiveness of the proposed approach

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

Joint sparsity-driven inversion and model error correction for radar imaging

Solution of inverse problems in imaging requires the use of a mathematical model of the observation process. However such models often involve errors and uncertainties themselves. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed image. Mostly, phase errors vary only in cross-range direction. However, in many situations, it is possible to encounter 2D phase errors, which are both range and cross-range dependent. We propose a sparsity-driven method for joint SAR imaging and correction of 1D as well as 2D phase errors. This method performs phase error correction during the image formation process and provides focused, high-resolution images. Experimental results show the effectiveness of the approach

Joint sparsity-driven inversion and model error correction for SAR imaging

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this thesis is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. In this technique, phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the proposed method for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

A sparsity-driven approach for SAR image formation and space-variant focusing (SAR görüntü oluşturma ve uzam değişir odaklama için seyreklik güdümlü bir yaklaşım)

In synthetic aperture radar (SAR) imaging, the uncertainties on the position of the sensing platform and on the motion of objects in the observed scene, are important problem sources. These types of uncertainties cause phase errors in the SAR data and subsequently defocusing in the formed image. The defocusing caused by the inexact knowledge of the position of the sensing platform is space-invariant, i.e., the amount of defocusing is same for all points in the scene. However, moving targets in the scene cause space-variant defocusing, i.e., the defocusing arises only in the parts of the image including the moving targets, whereas the stationary background is not defocused. To obtain a focused image, phase errors caused by the moving objects need to be removed. In scenarios involving of multiple point targets moving with different velocities in the scene, considering that the scene to be imaged is usually sparse, we present a sparsity-driven method for joint SAR imaging and removing the defocus caused by moving targets. The proposed method is based on the optimization of a cost function of both the image and phase errors, in a nonquadratic regularization based framework