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- W2889385280 abstract "In many scenarios, it is difficult to know the sparsity order of sparse signal in advance. However, sparsity order is a very vital parameter for many application. In this paper, we utilize the multiple measurement vector model in compressed sensing as the signal model of the sparsity order estimation to further improve the estimation performance. We first associate the sparsity order with the eigenvalues of the covariance matrix of the observed value. By exploiting the principal component analysis and slope analysis, a sparsity estimation method is proposed. Next, we use the sparsity order estimation error to measure the accuracy of the sparsity estimation algorithm. Finally, some simulations are carried out to verify the proposed method. The simulation results show that the proposed algorithm can accurately estimate the sparsity in the low SNR regime." @default.
- W2889385280 created "2018-09-07" @default.
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- W2889385280 date "2018-06-01" @default.
- W2889385280 modified "2023-09-25" @default.
- W2889385280 title "Sparsity Order Estimation Algorithm In Compressed Sensing by Exploiting Slope Analysis" @default.
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- W2889385280 doi "https://doi.org/10.1109/iwcmc.2018.8450522" @default.
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