A Correlation Coefficient Sparsity Adaptive Matching Pursuit Algorithm

Li, Yanjun; Chen, Wendong

doi:10.1109/lsp.2023.3252469

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…In order to reflect accurately and fully the correlation between various variables, Carl Pearson proposed the correlation coefficient [29,30]…”

Section: Pearson Correlation Coefficientmentioning

confidence: 99%

Flexible grounding system fault location method based on steady state projection

Liu,

et al. 2024

IEICE Electron. Express

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At present, the grounding protection of flexible grounding system mostly directly adopts the zero-sequence time-limit overcurrent protection of low-resistance grounding system, which has problems such as insufficient protection sensitivity when grounding via high-resistance. The changes in zero-sequence voltage (ZSV) and zero-sequence current (ZSC) upstream and downstream of the fault point before and after the small resistor is put into operation are analyzed. The fault information collected by the synchronous phasor measurement unit (PMU) are orthogonalized. Finally, the correlation coefficient method is utilized to compare the numerical values and polarity of projection amounts between adjacent PMUs. Based on this, a fault location method based on steady-state projection is proposed in this paper. Simulation and on-site test data show that the proposed method can accurately identify the fault section under different fault conditions.

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“…In order to reflect accurately and fully the correlation between various variables, Carl Pearson proposed the correlation coefficient [29,30]…”

Section: Pearson Correlation Coefficientmentioning

confidence: 99%

Flexible grounding system fault location method based on steady state projection

Liu,

et al. 2024

IEICE Electron. Express

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“…Compared with convex optimization class and statistical class based on Bayesian framework, greedy class algorithm has lower computational complexity. At present, many classical greedy algorithms have been proposed [14][15][16][17][18][19] , which are divided into different types by atom addition phase, deletion phase and termination criteria.…”

Section: Introductionmentioning

confidence: 99%

MUSIC subspace pursuit algorithm for DOA estimation with single snapshot

Cao,

Zhao,

Gao

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Direction of arrival (DOA) estimation on uniform linear array with single snapshot has always been a hot topic in radar signal processing. The traditional subspace class estimation methods, such as multiple signal classification (MUSIC), and sparse recovery algorithms, such as subspace pursuit (SP) algorithm, are suitable for different situations, meanwhile have certain requirements for observation data and signal-to-noise ratio (SNR). Combining the advantages of these two methods, a novel MUSIC subspace pursuit (MSP) algorithm is proposed in this paper. Firstly, an error evaluation function of the residual signal is constructed. The angles measured by both methods are replaced and selected one by one. And then, the framework of the greedy algorithm is iteratively applied until convergence. In this algorithm, the subspace information of the signal is added to the greedy algorithm, and more conditions are used to improve the angle measurement accuracy with single snapshot. Simulation results illustrate that the proposed MSP algorithm outperforms the SP and MUSIC algorithms at different SNR levels and it keeps the faster reconstruction speed of SP algorithm.

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