引用本文: | 林波,张增辉,朱炬波.L1-analysis稀疏重构在阵列信号恢复及波达角估计中的应用.[J].国防科技大学学报,2013,35(5):152-157.[点击复制] |
LIN Bo,ZHANG Zenghui,ZHU Jubo.Reconstruction of array output and direction-of-arrival estimation via L1-analysis sparse recovery[J].Journal of National University of Defense Technology,2013,35(5):152-157[点击复制] |
|
|
|
本文已被:浏览 7390次 下载 6752次 |
L1-analysis稀疏重构在阵列信号恢复及波达角估计中的应用 |
林波, 张增辉, 朱炬波 |
(国防科技大学 理学院, 湖南 长沙 410073)
|
摘要: |
通过适当的空域稀疏化构造了可对阵列接收信号进行冗余稀疏表示的阵列流形矩阵,建立了相应的L1-analysis稀疏重构模型,用于恢复阵列接收信号,重点证明了该流形矩阵是满足L1-analysis稀疏重构条件的紧框架,从理论上保证了将L1 -analysis稀疏重构用于阵列接收信号恢复及波达角估计问题的合理性,并推导出信号恢复误差的理论上界。利用在微波暗室环境中采集的实测数据,结合MUSIC算法进行实验验证,结果表明基于L1 -analysis稀疏重构的信号恢复对提高低信噪比环境下的波达角估计性能是有效的。 |
关键词: L1 -analysis稀疏重构;冗余稀疏表示 阵列信号处理 波达角估计;低信噪比 |
DOI: |
投稿日期:2013-02-12 |
基金项目:国家自然科学基金资助项目(60901071,61002024,61102169,61201332);国防科学技术大学科研计划项目(JC11-02-03) |
|
Reconstruction of array output and direction-of-arrival estimation via L1-analysis sparse recovery |
LIN Bo, ZHANG Zenghui, ZHU Jubo |
(College of Science, National University of Defense Technology, Changsha 410073, China)
|
Abstract: |
The array manifold matrix was constructed as a redundant dictionary in which the array receiving signals were sparse through the appropriate spatial sparse division, and the corresponding L1-analysis sparse recovery model was established to reconstruct the array output data. The core of this paper is to prove that the manifold matrix is a tight frame and can satisfy the condition which guarantees the accurate recovery of signals through L1-analysis sparse recovery so that it is reasonable enough to use L1-analysis sparseness optimization to reconstruct the array output data. The upper bound of reconstruction error was given. The effectiveness of this presented method for improving the performance of DOA estimation with low SNR was verified by the experiments using the actual measurement data received in microwave darkroom through MUSIC algorithm. |
Keywords: L1-analysis sparse recovery redundant sparse representation array signal processing DOA estimation low Signal-to-Noise Ratio |
|
|