ℓ2,-correlation and robust matching pursuit for sparse approximation

Sun, Weize; Huang, Yingying; Li, Qiang; Zhang, Jihong

doi:10.1016/j.dsp.2020.102761

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…In recent years, people have invested a lot of energy in researching high-resolution technology to estimate the angle of arrival of sources on linear arrays in wireless communications, including astronomy, radar, smart grid, and sonar [1][2][3][4][5][6][7]. Algorithms like MUSIC [8] and ESPRIT [9] are based on subspace decomposition to solve uncorrelated signals with good performance.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Approach for DOA Estimation of Coherent Signals

Liu

Cao

et al. 2022

Wireless Communications and Mobile Computing

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An algorithm for estimation of direction of arrival (DOA) based on QR decomposition is proposed for coherent signals in this paper. When coherent is present, the rank of the signal covariance matrix is generally less than the signal number, making the estimation of the signal or noise subspace inaccurate. Therefore, we need to eliminate the spatial covariance matrix rank loss. According to the idea of matrix reconstruction, we try to construct three different data matrices, one is the signal covariance matrix, the other is the eigenvector reconstruction matrix of the signal covariance matrix, and the other is reconstructed matrix with the addition of spatial smoothing technology. Based on the resulting data matrix, whereafter, QR decomposition with the iteratively weighted least squares (IWLS) as solver is proposed to reduce the computational complexity of DOA estimation. Compared with other existing algorithms, the simulation results show that our method has high accuracy and great increase in the computational efficiency.

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Section: Introductionmentioning

confidence: 99%

Fast and Accurate Approach for DOA Estimation of Coherent Signals

Liu

Cao

et al. 2022

Wireless Communications and Mobile Computing

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“…As shown above, the existing works either define a capped norm directly as a loss or apply the capped operation on elements or vectors. In fact, in [51], the authors defined a capped l 2,1 -norm of a matrix. In this paper, we introduce a general capped norm of a matrix, named capped l p,q -norm.…”

Section: Introductionmentioning

confidence: 99%

Capped norm linear discriminant analysis and its applications

Liu¹,

Xiong

Ren³

et al. 2020

Preprint

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Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the robustness of LDA, in this paper, we introduce capped l2,1-norm of a matrix, which employs nonsquared l2-norm and "capped" operation, and further propose a novel capped l2,1-norm linear discriminant analysis, called CLDA. Due to the use of capped l2,1-norm, CLDA can effectively remove extreme outliers and suppress the effect of noise data. In fact, CLDA can be also viewed as a weighted LDA. CLDA is solved through a series of generalized eigenvalue problems with theoretical convergency. The experimental results on an artificial data set, some UCI data sets and two image data sets demonstrate the effectiveness of CLDA.

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ℓ2,-correlation and robust matching pursuit for sparse approximation

Cited by 2 publications

References 37 publications

Fast and Accurate Approach for DOA Estimation of Coherent Signals

Fast and Accurate Approach for DOA Estimation of Coherent Signals

Capped norm linear discriminant analysis and its applications

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