High‐quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks

Zhang, Ye; Yang, Qi; Zeng, Yifan; Deng, Bin; Wang, Hongqiang; Qin, Yuliang

doi:10.1002/mop.32411

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…The framework of enhanced MMW 3-D imaging via a CVFCNN is shown in Figure 1. This framework is similar to the enhanced radar imaging via neural network used in [14][15][16]30,31], while the initial imaging method and the structure of a CVFCNN are different. The algorithm is divided into the training stage and the testing stage.…”

Section: Framework Of Enhanced Mmw Imaging Via Cvfcnnmentioning

confidence: 99%

“…Even though research into CVNNs is difficult, in recent years there have been good results for the research of radar imaging. A CVNN was used for 2-D inverse SAR imaging to achieve efficient image enhancement in [30,31]. For undersampling SAR imaging, an algorithm with motion-compensation through a CVNN was proposed in [32] to eliminate the effect of motion errors on imaging results.…”

Section: Introductionmentioning

confidence: 99%

“…To isolate moving targets from stationary clutter and refocus the target images, a novel CVNN-based method was proposed in [33] for GMTI, using a multichannel SAR system. With regard to the structure of neural networks, studies have shown that the use of a fully convolutional network can reduce the complexity of the model and avoid the limitations of image size [15,31,34]. In this paper, we propose a complex-valued fully convolutional neural network (CVFCNN) with a generalized form of complex parametric rectified linear unit (CPReLU) activation function for MMW 3-D imaging.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network

Jing

Miao

et al. 2022

Electronics

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To solve the problems of high computational complexity and unstable image quality inherent in the compressive sensing (CS) method, we propose a complex-valued fully convolutional neural network (CVFCNN)-based method for near-field enhanced millimeter-wave (MMW) three-dimensional (3-D) imaging. A generalized form of the complex parametric rectified linear unit (CPReLU) activation function with independent and learnable parameters is presented to improve the performance of CVFCNN. The CVFCNN structure is designed, and the formulas of the complex-valued back-propagation algorithm are derived in detail, in response to the lack of a machine learning library for a complex-valued neural network (CVNN). Compared with a real-valued fully convolutional neural network (RVFCNN), the proposed CVFCNN offers better performance while needing fewer parameters. In addition, it outperforms the CVFCNN that was used in radar imaging with different activation functions. Numerical simulations and experiments are provided to verify the efficacy of the proposed network, in comparison with state-of-the-art networks and the CS method for enhanced MMW imaging.

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Section: Framework Of Enhanced Mmw Imaging Via Cvfcnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network

Jing

Miao

et al. 2022

Electronics

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“…Besides, THz wave has the ability of imaging at higher frame rates, which makes it be utilized in video synthetic aperture radar (ViSAR) imaging. These advantages make THz radar imaging technique have promising application prospects in maneuvering target surveillance and recognition in space and near space [9], [10], and ground moving target detection and tracking [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Estimation of High-Frequency Vibration Parameters for Terahertz SAR Imaging Based on FrFT With Combination of QML and RANSAC

et al. 2021

IEEE Access

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High-frequency vibration of motion platform leads to paired echo for synthetic aperture radar (SAR) imaging, especially in terahertz band due to its shorter wave length. Different from most existing parameters estimation methods only considering single component high-frequency vibration, in this paper a novel method considering multi-components vibration model is proposed based on fractional Fourier transform (FrFT) with combination of quasi-maximum likelihood (QML) and random sample consensus (RANSAC). Based on the model establishment of high-frequency vibration error in the echo, its instantaneous chirp rate (ICR) is firstly estimated by FrFT in sliding sub-aperture, followed which the vibration parameters are coarsely obtained through spectrum analysis and least square (LS) regression. To further refine the parameters estimates, QML is developed for compensating the deviation both caused by the frequency spectrum leakage and the error propagation effects by one-dimensional search over the vibration frequency. Meanwhile, RANSAC is adopted for avoiding the outlier of the ICR estimates in LS regression, especially at low signal-to-noise ratio (SNR). Thus, the refinement strategy based on the combination of QML and RANSAC is developed, whose utilization improves the estimation accuracy of vibration parameters. Finally, the paired echo in terahertz SAR (THz-SAR) imaging is effectively suppressed by the proposed method, and the high-quality THz-SAR imaging results are achieved. Both simulations of single component and multi-components high-frequency vibration are used to verify the validity of the proposed method. The simulation results show that the proposed method has higher estimation accuracy even at low SNR.INDEX TERMS Fractional Fourier transform (FrFT), high-frequency vibration, quasi-maximum likelihood (QML), random sample consensus (RANSAC), terahertz synthetic aperture radar (THz-SAR).

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“…Regarding the target image reconstruction in sparse imaging, the algorithm based on low rank matrix recovery (LRMR) technique with principal component pursuit by alternating directions method (PCPADM) has been investigated in this paper. Compared to emergent deep learning based image reconstruction approach, image reconstructions based on low rank matrix completion (LRMC) or LRMR exempt from training stage with use of large amount of data [29]- [31]. Moreover, the network is only valid for one specific imaging model after trained [31].…”

mentioning

confidence: 99%

Multistatic MIMO Sparse Imaging Based on FFT and Low-Rank Matrix Recovery Techniques

Molaei

Yurduseven

et al. 2023

IEEE Trans. Microwave Theory Techn.

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This paper proposes a simple sparse imaging scheme of using a linear sparse aperiodic array and a new fast Fourier transform (FFT) matched filtering algorithm for a THz multistatic multiple-input and multiple-output (MIMO) imaging system. The simple linear sparse aperiodic array and mutlipass interferometric synthetic aperture focusing technique are used to achieve a fast sampling, low system cost and high imaging performance. Unlike a traditional generalized synthetic aperture focusing technique (GSAFT) for multistatic MIMO imaging, which is time consuming and exhibits increased reconstruction time with increased data volume, the proposed FFT matched filtering (FFTMF) image reconstruction algorithm is capable of providing comparable image quality but significantly reducing the reconstruction time. For example, we show that for an image of 300 mm × 320 mm with a pixel size of 0.75 mm × 0.83 mm, the reconstruction time is reduced from about 1.50 minutes to 0.25 s in the 220 GHz 5-pass synthetic imaging experiments. The proposed imaging algorithm uses an internal zero padding, a multipass interferometric synthetic aperture focusing technique and a wideband imaging technique to improve the imaging performance under a low-cost, sparse sampling scheme. It shows a strong anti-noise ability and a high tolerance to target focusing distance. In addition, integrated with a algorithm of principal component pursuit by alternating directions method (PCPADM), sparse imaging is available to further save system cost and sampling data without a loss of image quality while the novel use of error matrix provides an additional detection capability for imaging systems.

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High‐quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks

Cited by 4 publications

References 8 publications

Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network

Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network

Estimation of High-Frequency Vibration Parameters for Terahertz SAR Imaging Based on FrFT With Combination of QML and RANSAC

Multistatic MIMO Sparse Imaging Based on FFT and Low-Rank Matrix Recovery Techniques

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