An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework
An Improved Auto-Calibration Algorithm Based on Sparse Bayesian Learning Framework
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DOI：
10.1109/lsp.2013.2272462
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发表时间：
2013-07
影响因子：
3.9
通讯作者：
Lifan Zhao;G. Bi;Lu Wang;Haijian Zhang
中科院分区：
文献类型：
--
作者：
Lifan Zhao;G. Bi;Lu Wang;Haijian Zhang
这封信考虑了压缩感知中的乘性扰动问题，这已经成为一个越来越重要的问题，以获得实际应用的鲁棒性能。将该问题转化为概率模型，提出了一种自校准稀疏贝叶斯学习算法。在该算法中，信号和扰动迭代估计，以实现稀疏利用变分贝叶斯期望最大化技术。数值实验结果表明，该算法在信号重构精度上有所提高。
This letter considers the multiplicative perturbation problem in compressive sensing, which has become an increasingly important issue on obtaining robust performance for practical applications. The problem is formulated in a probabilistic model and an auto-calibration sparse Bayesian learning algorithm is proposed. In this algorithm, signal and perturbation are iteratively estimated to achieve sparsity by leveraging a variational Bayesian expectation maximization technique. Results from numerical experiments have demonstrated that the proposed algorithm has achieved improvements on the accuracy of signal reconstruction.