Two-Dimensional Superresolution Inverse Synthetic Aperture Radar Imaging Using Hybridized SVSV Algorithm

Qadir, Shahida G.; Fan, Yangyu

doi:10.1109/tap.2012.2224832

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…These methods, whose use has increased significantly during the recent years, are reviewed in Section VII-A. Other examples of applying non-linear slow-time filtering in ISAR include the Capon method [193], the iterative minimum means squares error (MMSE) method [194], the weighted least squares-based iterative adaptive approach [195], and super-resolution techniques [196]. These methods have been shown surpass the RD and RID methods in terms of image resolution in specific examples.…”

Section: Other Methodsmentioning

confidence: 99%

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Vehmas¹,

Neuberger

2021

IEEE Access

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Radar can obtain high spatial resolution at large operating distances and its operation is largely independent from weather and lighting conditions. This makes it an important instrument in an ever-increasing number of applications requiring robust and reliable sensing of the environment. Due to the advancements in semiconductor technology and digital computation power, radar can obtain increasingly better position estimation accuracy, detection sensitivity, and target resolution. High-resolution imaging is an example of a rapidly advancing field of radar technology. Inverse synthetic aperture radar (ISAR) is an imaging technique that uses the motion of a target to obtain a high-resolution radar image of it. This paper presents a comprehensive review of past work and the state-of-the-art in ISAR imaging systems, algorithms, and applications. Starting from the early historical developments of simple monostatic range-Doppler imaging, we present the developments up to the current advanced spatially distributed imaging modalities and reconstruction algorithms. The list of references includes the most influential ISAR publications in the open literature up to early 2021.

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Section: Other Methodsmentioning

confidence: 99%

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Vehmas¹,

Neuberger

2021

IEEE Access

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“…The Range Doppler (RD) algorithm [4], which is supported by the 2D Fast Fourier Transform (FFT), is the most widely applied ISAR imaging algorithm. However, the radar image achieved by the RD algorithm often suffers from high sidelobe and low resolution [5], which hardly meets the requirement of newly developed Automatic Targets Recognition (ATR) techniques. Additionally, complicated motion of targets and low Signal to Noise Ratio (SNR) condition further deteriorate the performance of the RD algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Zhang

Liu

et al. 2016

Sensors

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This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

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“…igh-resolution radar imaging [1][2][3][4][5][6][7][8][9][10], including synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), is an important technology, which is extensively used in many applications, such as target recognition and space target surveillance. Generally, high-resolution ISAR image of moving targets, e.g., aircrafts, ships and satellites, can be obtained by range-Doppler (RD) method [6].…”

Section: Introductionmentioning

confidence: 99%

“…In practice, RD method, based on Fourier transform, is known to be robust but suffers from limited resolution and high side lobes in the cases of limited pulses and maneuvering targets. Therefore, many modern spectral estimation techniques [7][8][9] and sparse signal analysis methods [10] are proposed for ISAR imaging. In ISAR imaging, high range resolution can usually be achieved by This work is partially supported by the National Natural Science Foundation of China (61271291, 61271024, 61201292, 61201283) and the Fundamental Research Funds for the Central Universities.…”

Section: Introductionmentioning

confidence: 99%

Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Jiu

Liu

et al. 2015

IEEE Trans. Antennas Propagat.

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Abstract-Compressive sensing (CS) is successfully applied in inverse synthetic aperture radar (ISAR) imaging. But, as target rotation rate is not concerned in the CS-based imaging methods, the obtained image cannot be scaled in the cross-range dimension. Consequently, difficulties arise in extracting the target geometrical information from the CS ISAR image. But, target geometrical size is an important parameter in automatic radar target recognition. To remedy this problem, a joint ISAR imaging and cross-range scaling method is proposed. In the proposed method, an adaptive parametric dictionary, comprising chirp rate parameter, is used to represent the observed data. By minimizing the reconstruction error, sparsity-constrained optimization, combined with the chirp-rate parameter and target reflective coefficient, is established. To find a solution to the nonlinear and nonconvex optimization problem, an iterative procedure is developed. Finally, with the help of the chirp-rate, target rotation rate can be estimated by the least square method, and the ISAR image can be scaled in cross-range. Experimental results show that the proposed method can fit the observed data better than the method using a fixed Fourier dictionary. Besides, cross-range scaled ISAR images can be obtained with limited pulses.Index terms-Compressive sensing, inverse synthetic aperture radar (ISAR), adaptive dictionary, cross-range scaling.

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Two-Dimensional Superresolution Inverse Synthetic Aperture Radar Imaging Using Hybridized SVSV Algorithm

Cited by 8 publications

References 8 publications

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

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