128 research outputs found

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

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    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

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

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    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

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    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

    A Generalized Phase Gradient Autofocus Algorithm

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    The phase gradient autofocus (PGA) algorithm has seen widespread use and success within the synthetic aperture radar (SAR) imaging community. However, its use and success has largely been limited to collection geometries where either the polar format algorithm (PFA) or range migration algorithm is suitable for SAR image formation. In this work, a generalized phase gradient autofocus (GPGA) algorithm is developed which is applicable with both the PFA and backprojection algorithm (BPA), thereby directly supporting a wide range of collection geometries and SAR imaging modalities. The GPGA algorithm preserves the four crucial signal processing steps comprising the PGA algorithm, while alleviating the constraint of using a single scatterer per range cut for phase error estimation which exists with the PGA algorithm. Moreover, the GPGA algorithm, whether using the PFA or BPA, yields an approximate maxi- mum marginal likelihood estimate (MMLE) of phase errors having marginalized over unknown complex-valued reflectivities of selected scatterers. Also, in this work a new approximate MMLE, termed the max-semidefinite relaxation (Max-SDR) phase estimator, is proposed for use with the GPGA algorithm. The Max-SDR phase estimator provides a phase error estimate with a worst-case approximation bound compared to the solution set of MMLEs (i.e., solution set to the non-deterministic polynomial- time hard (NP-hard) GPGA phase estimation problem). Moreover, in this work a specialized interior-point method is presented for more efficiently performing Max- SDR phase estimation by exploiting low-rank structure typically associated with the GPGA phase estimation problem. Lastly, simulation and experimental results produced by applying the GPGA algorithm with the PFA and BPA are presented

    Backprojection Autofocus for Synthetic Aperture Radar

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    In synthetic aperture radar (SAR), many adverse conditions may cause errors in the raw phase-history data. Autofocus methods are commonly used in SAR to mitigate the effects of these problems. Over the years, many types of autofocus have algorithms have been created, however, each has implicit assumptions restricting their use. The backprojection image formation algorithm places few restrictions on SAR imaging, thus it is desirable to have an autofocus algorithm that is similarly unconstrained. This paper presents a versatile autofocus method that is accordant with backprojection

    Bayesian super-resolution with application to radar target recognition

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    This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data

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

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    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

    Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

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    High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics
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