scholarly journals Sparse Signal Recovery for Direction-of-Arrival Estimation Based on Source Signal Subspace

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Lin ◽  
Jiying Liu ◽  
Meihua Xie ◽  
Jubo Zhu
Keyword(s):  
Computational Cost ◽  
Real Data ◽  
Direction Of Arrival ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Signal Subspace ◽  
Second Order Cone ◽  
Source Signal ◽  
Improved Accuracy

After establishing the sparse representation of the source signal subspace, we propose a new method to estimate the direction of arrival (DOA) by solving anℓ1-norm minimization for sparse signal recovery of the source powers. Second-order cone programming is applied to reformulate this optimization problem, and it is solved effectively by employing the interior point method. Due to the keeping of the signal subspace and the discarding of the noise subspace, the proposed method is more robust to noise than many other sparsity-based methods. The real data tests and the numerical simulations demonstrate that the proposed method has improved accuracy and robustness to noise, and it is not sensitive to the knowledge about the number of sources. We discuss the computational cost of our method theoretically, and the experiment results verify the computational effectiveness.

scholarly journals A Reweighted Symmetric Smoothed Function Approximating L0-Norm Regularized Sparse Reconstruction Method

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 583 ◽  
Author(s):  
Jianhong Xiang ◽  
Huihui Yue ◽  
Xiangjun Yin ◽  
Guoqing Ruan
Keyword(s):  
Objective Function ◽  
Numerical Experiments ◽  
State Of The Art ◽  
Real Data ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Norm Minimization ◽  
Noisy Conditions

Sparse-signal recovery in noisy conditions is a problem that can be solved with current compressive-sensing (CS) technology. Although current algorithms based on L 1 regularization can solve this problem, the L 1 regularization mechanism cannot promote signal sparsity under noisy conditions, resulting in low recovery accuracy. Based on this, we propose a regularized reweighted composite trigonometric smoothed L 0 -norm minimization (RRCTSL0) algorithm in this paper. The main contributions of this paper are as follows: (1) a new smoothed symmetric composite trigonometric (CT) function is proposed to fit the L 0 -norm; (2) a new reweighted function is proposed; and (3) a new L 0 regularization objective function framework is constructed based on the idea of T i k h o n o v regularization. In the new objective function framework, Contributions (1) and (2) are combined as sparsity regularization terms, and errors as deviation terms. Furthermore, the conjugate-gradient (CG) method is used to optimize the objective function, so as to achieve accurate recovery of sparse signal and image under noisy conditions. The numerical experiments on both the simulated and real data verify that the proposed algorithm is superior to other state-of-the-art algorithms, and achieves advanced performance under noisy conditions.

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 Sparse signal recovery based on adaptive algorithms for debris detector

AIP Advances ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 065131
Author(s):  
Bingsen Xue ◽  
Xingming Zhang ◽  
Yunzhe Xu ◽  
Yehui Li ◽  
Hongpeng Zhang ◽  
...  
Keyword(s):  
Adaptive Algorithms ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery

Improved RIP‐based performance guarantee for sparse signal recovery via A*OMP

2018 ◽  
Vol 54 (21) ◽  
pp. 1216-1218 ◽  
Author(s):  
Haifeng Li ◽  
Guoqi Liu ◽  
Jian Zou
Keyword(s):  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Performance Guarantee
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