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- W2897948008 abstract "With the widespread use of high-dimensional data, algorithms dealing with sparsity signal models become very significant. There are some algorithms for sparsity signal models, but relatively few algorithms for dynamic filtering of time-vary sparse signals. In this work, we present a algorithm based on Block Coordinate Decent (BCD), which is a variant of the Reweighted $ell_{1}$ Minimization (RWL1) to deal with dynamic filtering of time-vary sparse signals. The proposed algorithm has a good convergence. It is more robust than the Basis Pursuit De-Noising Dynamic Filtering (BPDN-DF) which relies on the predicted accuracy of dynamic filtering. Moreover, the numerical results show the superior performance of our algorithm compared to existing methods." @default.
- W2897948008 created "2018-10-26" @default.
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- W2897948008 date "2018-07-01" @default.
- W2897948008 modified "2023-09-26" @default.
- W2897948008 title "Dynamic Filtering of Sparse Signals via <tex>$ell_{1}$</tex> Minimization with Variant Parameters" @default.
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- W2897948008 doi "https://doi.org/10.23919/chicc.2018.8482547" @default.
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