scholarly journals Compressed Sensing Imaging for Staggered SAR with Low Oversampling Rate

2020 ◽  
Author(s):  
Xingxing Liao ◽  
Changlin Jin ◽  
Zhe Liu
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Synthetic Aperture Radar ◽  
Fast Fourier Transform ◽  
Synthetic Aperture ◽  
Data Simulation ◽  
Iterative Shrinkage ◽  
Simulation Results ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

This paper focuses on processing low oversampling echo data of staggered synthetic aperture radar (SAR). In staggered mode, the non-uniformly sampling and irregular loss of echo data cause azimuth ambiguity which severely degrades the imaging quality. To solve this problem, we propose a compressed sensing (CS) method in which the non-uniform fast Fourier transform (NUFFT) technique is adopted to obtain uniform azimuth spectrum, and the fast iterative shrinkage thresholding algorithm (FISTA) is utilized to efficiently reconstruct the ambiguity-free image from in-complete echo data. Simulation results demonstrate the proposed method can effectively suppress the azimuth ambiguity in the vicinity of targets.

An Adaptive Regularized Fast Iterative Shrinkage-Thresholding Algorithm for Image Reconstruction in Compressed Sensing

2013 ◽  
Vol 710 ◽  
pp. 593-597
Author(s):  
Xin Meng ◽  
Shi Fang Duan ◽  
She Xiang Ma
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Objective Function ◽  
Time Complexity ◽  
Regularization Parameter ◽  
Iteration Process ◽  
Reconstructed Image ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing, this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm. This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function. During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value. Simulation results show that the proposed algorithm, compared with the traditional algorithms, obtains the better reconstructed image quality and lower time complexity.

scholarly journals A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yudong Zhang ◽  
Jiquan Yang ◽  
Jianfei Yang ◽  
Aijun Liu ◽  
Ping Sun
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Computation Time ◽  
Resonance Imaging ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time.Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use.Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift.Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio.Conclusion. EWISTARS is superior to state-of-the-art approaches.

scholarly journals Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Rui Zhang ◽  
Chen Meng ◽  
Cheng Wang ◽  
Qiang Wang
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Fractional Fourier Transform ◽  
Radar Signal ◽  
Reconstruction Algorithms ◽  
Echo Signal ◽  
Time Frequency ◽  
Iterative Shrinkage ◽  
Radar Echo ◽  
Iterative Shrinkage Thresholding

The compressed sensing theory, which has received great attention in the field of radar technology, can effectively reduce the data rate of high-resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems. Through the study of radar signal processing theory, it can be found that the echo of radar LFM transmit signal has sparse characteristics in the distance upward; based on this, we can consider using the theory of compressed sensing in the processing of radar echo to optimize the processing. In this paper, a fast iterative shrinkage-thresholding reconstruction algorithm based on protection coefficients is proposed. Under the new scheme, firstly, the LFM echo signal’s good sparse representation is obtained by using the time-frequency sparse characteristics of the LFM echo signal under the fractional Fourier transform; all reconstruction coefficients are analyzed in the iterative process. Then, the coefficients related to the feature will be protected from threshold shrinkage to reduce information loss. Finally, the effectiveness of the proposed method is verified through simulation experiments and application example analysis. The experimental results show that the reconstruction error of this method is lower and the reconstruction effect is better compared with the existing reconstruction algorithms.

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