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- W4319586626 abstract "Sparse Bayesian learning (SBL) is a robust algorithm for sparse signal reconstruction (SSR) and widely applied in many fields. Unfortunately, the conventional SBL algorithm suffers from heavy computational complexity due to the matrix inversion involved in each iteration, which is proportional to the cube of the observed data length and thus prevents it solving large-scale data. In this paper, based on the Fourier Dictionary (FD)-based SBL, abbreviated as FD-SBL, we propose an efficient FD-SBL method to implement SSR for uniformly sampled one-dimensional (1D) case. By utilizing the property that the matrix to be inverted is a Toeplitz matrix, we can exploit the Gohberg-Semencul (G-S)-type factorization to reduce the computational complexity of the FD-SBL algorithm. To be noted, there is no approximation in our proposed method, and the computational cost is reduced by several orders of magnitude compared with the conventional SBL algorithm. Finally, the experimental results verify the effectiveness of the proposed method." @default.
- W4319586626 created "2023-02-09" @default.
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- W4319586626 date "2021-12-15" @default.
- W4319586626 modified "2023-09-30" @default.
- W4319586626 title "Fast Implementation of Signal Reconstruction based on Sparse Bayesian Learning with Fourier Dictionary" @default.
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- W4319586626 doi "https://doi.org/10.1109/radar53847.2021.10028121" @default.
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