| 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.[点击复制] |
| 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[点击复制] |
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| 改进Laplace先验下的复数域多任务贝叶斯压缩感知方法 |
| 张启雷1,孙斌2 |
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(1. 国防科技大学 电子科学学院, 湖南 长沙 410073;2. 北京跟踪与通信技术研究所, 北京 100094)
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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。 |
| 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 |
| DOI：10.11887/j.cn.202305017 |
| 投稿日期：2021-05-25 |
| 基金项目:国家自然科学基金资助项目(62271495,61771478) |
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| Complex multitask Bayesian compressive sensing algorithm using modified Laplace priors |
| ZHANG Qilei1, SUN Bin2 |
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(1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China;2. 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. |
| Keywords: Bayesian compressive sensing multitask learning modified Laplace priors complex Bayesian compressive sensing |
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