Multifrequency Compressed Sensing for 2-D Near-Field Synthetic Aperture Radar Image Reconstruction

Bi, Dongjie; Xie, Yongle; Ma, Lan; Yang, Xiahan; Zheng, Yu

doi:10.1109/tim.2017.2654578

Cited by 25 publications

(10 citation statements)

References 44 publications

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“…The received signal provides 1D information about the transformer. Additional information to create a 2D image from the winding can be provided by changing the position of the antennas and repeating the process of sending and receiving pulses in different positions alongside the height of the transformer [4,16,17].…”

Section: Sar Imaging Methodsmentioning

confidence: 99%

Design and implementation of dielectric windows for detection of radial deformation of HV transformer winding using radar imaging

Mahmoodi

Abadi

Karami

et al. 2020

IET Science, Measurement & Technology

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Section: Sar Imaging Methodsmentioning

confidence: 99%

Design and implementation of dielectric windows for detection of radial deformation of HV transformer winding using radar imaging

Mahmoodi

Abadi

Karami

et al. 2020

IET Science, Measurement & Technology

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“…As an optimization algorithm, the linear Bregman iteration is widely used in the fields of compressed sensing [ 24 ], image de-noising [ 25 ], target detection [ 26 ], and quantitative clustering [ 27 ]. It has been one of the most effective methods for solving norm optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

Liu

Shan

Wang

2018

Sensors

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One of the main challenges faced by wireless sensor network (WSN) localization is the positioning accuracy of the WSN node. The existing algorithms are arduous to use for dealing with the pulse noise that is universal and ineluctable in practical considerations, resulting in lower positioning accuracy. Aimed at this problem and introducing Bregman divergence, we propose in this paper a novel WSN localization algorithm via matrix completion (LBDMC). Based on the natural low-rank character of the Euclidean Distance Matrix (EDM), the problem of EDM recovery is formulated as an issue of matrix completion in a noisy environment. A regularized matrix completion model is established, smoothing the pulse noise by leveraging L1,2-norm and the multivariate function Bregman divergence is defined to solve the model to obtain the EDM estimator. Furthermore, node localization is available based on the multi-dimensional scaling (MDS) method. Multi-faceted comparison experiments with existing algorithms, under a variety of noise conditions, demonstrate the superiority of LBDMC to other algorithms regarding positioning accuracy and robustness, while ensuring high efficiency. Notably, the mean localization error of LBDMC is about ten times smaller than that of other algorithms when the sampling rate reaches a certain level, such as >30%.

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“…The Split Bregman method (SBM) proposed in [28] is a universal convex optimization algorithm for both l 1 -norm and TV-norm regularization problems. By the idea of decomposing the original problem into several subproblems worked out by Bregman Iteration (BI) [29,30], SBM has been widely utilized in the complex domain through the complex-to-real converting technique [31,32], e.g., MRI imaging [33], SAR imaging [34], forward-looking scanning radar imaging [35], SAR image super-resolution [36], and massive MIMO channel estimation [37]. However, SBM still has great potential in terms of both reconstruction performance and time cost considering the following two points: The original BI defined in the real domain may not make good use of the phase information for complex variables, which degrades the recovery accuracy; secondly, the converting technique quadruples the elements of the sensing matrix A to 2 m × 2 n , which consumes more memory and time within the iteration process.…”

Section: Introductionmentioning

confidence: 99%

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

Xiong

Zhao

Shi

et al. 2019

Sensors

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The Split Bregman method (SBM), a popular and universal CS reconstruction algorithm for inverse problems with both l1-norm and TV-norm regularization, has been extensively applied in complex domains through the complex-to-real transforming technique, e.g., MRI imaging and radar. However, SBM still has great potential in complex applications due to the following two points; Bregman Iteration (BI), employed in SBM, may not make good use of the phase information for complex variables. In addition, the converting technique may consume more time. To address that, this paper presents the complex-valued Split Bregman method (CV-SBM), which theoretically generalizes the original SBM into the complex domain. The complex-valued Bregman distance (CV-BD) is first defined by replacing the corresponding regularization in the inverse problem. Then, we propose the complex-valued Bregman Iteration (CV-BI) to solve this new problem. How well-defined and the convergence of CV-BI are analyzed in detail according to the complex-valued calculation rules and optimization theory. These properties prove that CV-BI is able to solve inverse problems if the regularization is convex. Nevertheless, CV-BI needs the help of other algorithms for various kinds of regularization. To avoid the dependence on extra algorithms and simplify the iteration process simultaneously, we adopt the variable separation technique and propose CV-SBM for resolving convex inverse problems. Simulation results on complex-valued l1-norm problems illustrate the effectiveness of the proposed CV-SBM. CV-SBM exhibits remarkable superiority compared with SBM in the complex-to-real transforming technique. Specifically, in the case of large signal scale n = 512, CV-SBM yields 18.2%, 17.6%, and 26.7% lower mean square error (MSE) as well as takes 28.8%, 25.6%, and 23.6% less time cost than the original SBM in 10 dB, 15 dB, and 20 dB SNR situations, respectively.

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Multifrequency Compressed Sensing for 2-D Near-Field Synthetic Aperture Radar Image Reconstruction

Cited by 25 publications

References 44 publications

Design and implementation of dielectric windows for detection of radial deformation of HV transformer winding using radar imaging

Design and implementation of dielectric windows for detection of radial deformation of HV transformer winding using radar imaging

Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

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