scholarly journals High-Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Junjie Feng ◽  
Yinan Sun ◽  
XiuXia Ji
Keyword(s):  
High Resolution ◽  
Optimization Problem ◽  
Signal Reconstruction ◽  
Variable Parameter ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Loop Structure ◽  
Recovery Algorithm ◽  
Isar Imaging

In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.

scholarly journals ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Junjie Feng
Keyword(s):  
Vector Model ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Negative Exponential Function ◽  
Multiple Measurement ◽  
Recovery Algorithm ◽  
Multiple Measurement Vector ◽  
Isar Imaging ◽  
Measurement Vector

A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction

2015 ◽  
Vol 63 ◽  
pp. 66-78 ◽  
Author(s):  
Vidya L. ◽  
Vivekanand V. ◽  
Shyamkumar U. ◽  
Deepak Mishra
Keyword(s):  
Compressed Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Rbf Network ◽  
Recovery Algorithm

scholarly journals A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2021 ◽  
Author(s):  
Yunfei Cheng ◽  
Yalan Ye ◽  
Mengshu Hou ◽  
Wenwen He ◽  
Yunxia Li ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Sparse Bayesian Learning ◽  
Recovery Algorithm

scholarly journals A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jianhong Xiang ◽  
Huihui Yue ◽  
Xiangjun Yin ◽  
Linyu Wang
Keyword(s):  
Signal Reconstruction ◽  
Real Data ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Sparse Signal Reconstruction ◽  
System Equation ◽  
Norm Minimization ◽  
Better Than

Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.

scholarly journals Backtracking based Joint-Sparse Signal Recovery for Distributed Compressive Sensing

2019 ◽  
Vol 9 (2) ◽  
pp. 3919-3922
Keyword(s):  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Joint Sparsity ◽  
Joint Reconstruction ◽  
Distributed Compressive Sensing ◽  
Efficient Recovery

In Distributed Compressive Sensing (DCS), the Joint Sparsity Model (JSM) refers to an ensemble of signals being jointly sparse. In [4], a joint reconstruction scheme was proposed using a single linear program. However, for reconstruction of any individual sparse signal using that scheme, the computational complexity is high. In this paper, we propose a dual-sparse signal reconstruction method. In the proposed method, if one signal is known apriori, then any other signal in the ensemble can be efficiently estimated using the proposed method, exploiting the dual-sparsity. Simulation results show that the proposed method provides fast and efficient recovery.

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