Parametric sparse representation method for ISAR imaging of rotating targets

Rao, Wei; Li, Gang; Wang, Xiqin; Xia, Xiang-Gen

doi:10.1109/taes.2014.120535

Cited by 54 publications

(30 citation statements)

References 21 publications

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“…The compressed sensing imaging algorithm is applied to focus image, so the OMP (orthogonal matching pursuit) algorithm is used to reconstruct the target location. The optimal reconstruction expression can be expressed as follows [18,19]:…”

Section: Least Squares Methods Parameter Estimationmentioning

confidence: 99%

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

Song¹,

Li²,

Qu³

et al. 2019

PIER C

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In recent years, FMCW CSAR (frequency modulation continue wave circular synthetic aperture radar) is more and more widely used in military reconnaissance and sea surface target recognition. However, due to the influence of external factors, it cannot move in an ideal uniform circular trajectory, resulting in low imaging resolution. In this paper, the problem of motion errors caused by nonuniform circular motion is analyzed, and the phenomenon of range unit broadening and sidelobe increase caused by nonuniform circular motion errors is simulated. The echo model is characterized by error parameters. Based on the compressed sensing imaging algorithm, motion error parameters are estimated by parametric sparse representation. The least squares method and gradient descent method are applied to estimate motion error parameters. Simulations are conducted to show that both of the methods can reach the goal that the motion compensation is realized. The result of simulations and measurement data demonstrate that the algorithm can correct nonuniform circular motion errors better and further improve the imaging resolution.

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Section: Least Squares Methods Parameter Estimationmentioning

confidence: 99%

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

Song¹,

Li²,

Qu³

et al. 2019

PIER C

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“…This technique, however, does not make full use of radar signal, resulting in the decreasing of the cross-range resolution. In order to overcome disadvantages of the time-frequency analysis, some researchers introduce the theory of compressed sensing into the ISAR images reconstruction [11][12][13][14], especially for removing the non-uniform rotational motion. These works construct a parametric sparse representation model for the ISAR signal matrix, where the basis matrix is constructed according to the target rotation rate.…”

Section: Introductionmentioning

confidence: 99%

“…These works construct a parametric sparse representation model for the ISAR signal matrix, where the basis matrix is constructed according to the target rotation rate. Some works show good RMC results for an aircraft with rotational acceleration [11][12][13]. In addition, some works also use the sparse distribution of scatterers to directly reconstruct a high resolution ISAR image [15][16][17] Furthermore, another motion compensation approach with the cubic-order processing procedure [18] is also an option.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

et al. 2018

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We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN) to focus the distorted inverse synthetic aperture radar (ISAR) image. In this paper, a coefficient estimator based on the artificial neural network (ANN) is firstly developed to solve the time-consuming rotational motion compensation (RMC) polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs). Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

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“…The parametric sparse representation technique has been utilized to ISAR imaging of rotating targets [26][27][28], SAR motion compensation [29], and moving target motion parameters estimation [12]. Differing from the previous work where the process flow starts from entire raw data, the proposed method in this paper is based on ROI data.…”

Section: Introductionmentioning

confidence: 99%

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

Chen

Zhang

et al. 2017

Remote Sensing

Self Cite

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Abstract:In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method.

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Parametric sparse representation method for ISAR imaging of rotating targets

Cited by 54 publications

References 21 publications

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

Motion Compensation Algorithm for Single Track FMCW Csar by Parametric Sparse Representation

A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

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