| 改进Laplace先验下的复数域多任务贝叶斯压缩感知方法 |
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| 引用本文： | 张启雷,孙斌.改进Laplace先验下的复数域多任务贝叶斯压缩感知方法[J].国防科技大学学报,2023,45(5):150-156. |
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| 作者姓名： | 张启雷 孙斌 |
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| 作者单位： | 国防科技大学 电子科学学院, 湖南 长沙 410073;北京跟踪与通信技术研究所, 北京 100094 |
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| 基金项目： | 国家自然科学基金资助项目(62271495,61771478) |
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| 摘 要： | 为了将现有的实数域贝叶斯压缩感知方法推广至复数域，利用改进Laplace先验假设，提出了一种复数域多任务贝叶斯压缩感知(complex multitask Bayesian compressive sensing using modified Laplace priors, CMBCS-MLP)方法，消除了测量噪声方差的影响，并推导了一种基于递归操作的快速算法。数值仿真表明：针对复数域稀疏信号重构问题，相比于现有方法，所提CMBCS-MLP方法具有更好的精确性和鲁棒性。
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| 关 键 词： | 贝叶斯压缩感知 多任务学习 改进Laplace先验 复数域贝叶斯压缩感知 |
| 收稿时间： | 2021/5/25 0:00:00 |
Complex multitask Bayesian compressive sensing algorithm using modified Laplace priors |
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| ZHANG Qilei,SUN Bin.Complex multitask Bayesian compressive sensing algorithm using modified Laplace priors[J].Journal of National University of Defense Technology,2023,45(5):150-156. |
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| Authors: | ZHANG Qilei SUN Bin |
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| Institution: | College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China; Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China |
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| Abstract: | To extend the existing real-valued BCS(Bayesian compressive sensing) framework to the complex-valued one, a CMBCS-MLP(complex multitask Bayesian compressive sensing algorithm using modified Laplace priors) was developed to eliminate the impact of measurement noise variance, and a fast algorithm based on sequential operations was further derived. It is demonstrated by numerical examples that the developed CMBCS-MLP algorithm is more accurate and robust than the existing algorithms in the complex sparse signal reconstructions. |
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| Keywords: | Bayesian compressive sensing multitask learning modified Laplace priors complex Bayesian compressive sensing |
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