Deep Learning for Passive Synthetic Aperture Radar

Yonel, Bariscan; Mason, Eric; Yazıcı, Birsen

doi:10.1109/jstsp.2017.2784181

Cited by 54 publications

(30 citation statements)

References 39 publications

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“…Therefore, the solution of the rotation ratio can be regarded as the optimal solution problem, with the minimum entropy function as the objective function and K ω Ω  as the variable. At present, there are many kinds of optimization methods available in machine learning and deep learning [33][34][35]. The simplest and most commonly used method is the gradient descent method [20,36], so we used this method to accurately estimate the rotation ratio.…”

Section: Accurate Rotation Ratio Estimation Based On Minimum Entropymentioning

confidence: 99%

A Novel Inverse Synthetic Aperture Radar Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

Wang

et al. 2018

Applied Sciences

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When inverse synthetic aperture radar (ISAR) imaging maneuvers targets, the azimuth echo of the target scattering point causes a Doppler frequency time-varying problem, which leads to the blurring and defocusing of the imaging results. Traditional imaging methods struggle to meet the imaging requirements for maneuvering targets due to a poor imaging effect or low efficiency. Given these challenges, a modified chirp Fourier transform (MCFT) imaging method is proposed in this paper, based on the specific relationship between the target rotation parameters and the radar echo signal parameters. Firstly, discrete chirp Fourier transform is used to quickly estimate the target’s coarse rotation ratio. Then, the minimum entropy function and gradient descent method are used to calculate the target’s accurate rotation ratio. Finally, the azimuth focusing image is accomplished by performing MCFT once on the azimuth echo signal using the accurate rotation ratio. This method avoids estimating and separating the sub-echo components one-by-one, considerably improves the imaging speed, and guarantees the best imaging quality by applying the global minimum entropy principle. The experimental results show that the proposed method effectively achieves the two-dimensional, high-quality, and fast imaging of maneuvering targets.

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Section: Accurate Rotation Ratio Estimation Based On Minimum Entropymentioning

confidence: 99%

A Novel Inverse Synthetic Aperture Radar Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

Wang

et al. 2018

Applied Sciences

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“…Recently, the data-driven deep-learning methods, which are realized by learning from the training data through the deep neural network (DNN), have shown their superority over the stat-of-the-art model-driven methods in many SAR research fileds, such as SAR imaging [15], SAR target recognition [16]- [18], SAR image segmentation [19]- [21] and so on. However, to the authors' knowledge, no related work about the research of the deep learning in the restoration of the SAR spectrum aliasing problem has been reported yet.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Restoration From Spectrum Aliasing by Deep Learning

Liu

Liao

2020

IEEE Access

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The diversity and the space-variance of the spectrum-aliasing effects in SAR bring challenges to the available model-driven restoration methods. In this paper, a hybrid data-driven and model-driven deep learning scheme is innovatively proposed to deal with this problem. In this scheme, the SAR image restoration is realize via a U-shaped deep neural network (DNN). Meanwhile, the model of the SAR echo data is used to generate the training data to learn the weighting parameters of the network. The DNN in this method is designed to suit the SAR spectrum-aliasing application. Due to the general SAR echo mathematical model is employed for training data construction, the spectrum aliasing features in different SAR systems can be accommodated and learned. Hence, the proposed method can work well for various SAR configurations. Simulation results using the real Radarsat-1 data as well as the synthetic data prove the effectiveness as well as the robustness of the proposed method.

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“…Literature proposes several CNN based solutions that use handcrafted CNNs [5], [8], [12], [13], [30] that are trained on SAR template images. Recently a Recurrent Neural Network is also suggested [31].…”

Section: Introduction Odern Warfare Requires High Performing Autommentioning

confidence: 99%

Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias‐Stamatis

Aouf

2019

IEEE Trans. Aerosp. Electron. Syst.

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We propose a multi-modal and multi-discipline data fusion strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar imagery. Our architecture fuses a proposed Clustered version of the AlexNet Convolutional Neural Network with Sparse Coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet which is 99.33% and 99.86% for the 3 and 10-class problems respectively.

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Deep Learning for Passive Synthetic Aperture Radar

Cited by 54 publications

References 39 publications

A Novel Inverse Synthetic Aperture Radar Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

A Novel Inverse Synthetic Aperture Radar Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

SAR Image Restoration From Spectrum Aliasing by Deep Learning

Fusing Deep Learning and Sparse Coding for SAR ATR

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