A Generalized Phase Gradient Autofocus Algorithm

Evers, Aaron; Jackson, Julie Ann

doi:10.1109/tci.2019.2899453

Cited by 44 publications

(15 citation statements)

References 51 publications

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“…We have compared the proposed method with existing postprocessing autofocus methods of PGA [ 5 ], GPGA [ 7 ], and ME [ 12 ]. Constant false alarm rate (CFAR) detection was used to select the strongest scatterers for GPGA, which is a modified algorithm of PGA.…”

Section: Resultsmentioning

confidence: 99%

“…The threshold for the initial loop, i.e.,

, would be a user-defined sufficiently large positive value, but small enough to achieve an appropriate number of features containing the main scatterers. There are various ways to select the main scatterers, such as the methods in [ 6 , 7 , 13 ]. Any of these methods would work to select the appropriate initial threshold.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The point target and random noise assumption are inappropriate to the scene that contains dominant targets close to each other in the azimuth direction. Quality phase gradient autofocus (QPGA) and generalized phase gradient autofocus (GPGA) are proposed to alleviate this problem [ 6 , 7 ]. However, both QPGA and GPGA use the limited window size, which also limits the bandwidth of the estimated phase error.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Lee

Jung

Kim

2021

Sensors

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Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

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Section: Resultsmentioning

confidence: 99%

“…The threshold for the initial loop, i.e.,

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Lee

Jung

Kim

2021

Sensors

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“…Then, for each sub-aperture data group, apply the PGA algorithm. In [19], a generalized PGA algorithm, which is suitable for use with the backprojection algorithm, is developed. Evers et al [20] the PGA algorithm is extended for SAR over arbitrary flight paths, including both near-field and bistatic collection geometry.…”

Section: Introductionmentioning

confidence: 99%

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

et al. 2021

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Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed.

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“…It extracts the phase error by detecting a common peak in each range cut. In [ 13 ], PGA is extended to a generalized phase gradient autofocus, which works for reconstruction methods including the polar format algorithm (PFA), range migration, and backprojection (BPA).…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D

Bülow

Birk

2020

Sensors

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Sonars are essential for underwater sensing as they can operate over extended ranges and in poor visibility conditions. The use of a synthetic aperture is a popular approach to increase the resolution of sonars, i.e., the sonar with its N transducers is positioned at k places to generate a virtual sensor with kN transducers. The state of the art for synthetic aperture sonar (SAS) is strongly coupled to constraints, especially with respect to the trajectory of the placements and the need for good navigation data. In this article, we introduce an approach to SAS using registration of scans from single arrays, i.e., at individual poses of arbitrary trajectories, hence avoiding the need for navigation data of conventional SAS systems. The approach is introduced here for the near field using the coherent phase information of sonar scans. A Delay and Sum (D&S) beamformer (BF) is used, which directly operates on pixel/voxel form on a Cartesian grid supporting the registration. It is shown that this pixel/voxel-based registration and the coherent processing of several scans forming a synthetic aperture yields substantial improvements of the image resolution. The experimental evaluation is done with an advanced simulation tool generating realistic 2D sonar array data, i.e., with simulations of a linear 1D antenna reconstructing 2D images. For the image registration of the raw sonar scans, a robust implementation of a spectral method is presented. Furthermore, analyses with respect to the trajectories of the sensor locations are provided to remedy possible grating lobes due to the gaping positions of the transmitter devices.

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A Generalized Phase Gradient Autofocus Algorithm

Cited by 44 publications

References 51 publications

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D

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