期刊文献+

窄带信号波达方向估计仿真实验 被引量:1

Simulation Experiment of Direction of Arrival Estimation of Narrow Band Signal
下载PDF
导出
摘要 波达方向估计是阵列信号处理一个重要的研究分支,广泛地应用于军事及民用领域。基于空域信号自身具备的稀疏性,压缩感知理论为窄带信号波达方向估计提供了一种新的思路。通过本文设计的仿真实验,学生可以建立远场窄带测向模型,并对基于块稀疏算法的窄带测向和相干非相干信号块稀疏混合测向仿真。 Signal direction of arrival estimation is an important research branch of array signal processing,which is widely used in military and civil fields.Based on the sparsity of spatial signals,compressed sensing theory provides a new idea for DOA estimation of narrowband signals.Through the simulation experiment designed in this paper,students can build a narrowband direction finding model in the far field,and simulate narrowband direction finding based on block sparse algorithm and coherent incoherent signal block sparse mixed direction finding.
作者 廖昌俊 潘晔 刘继芝 LIAO CHang-jun;PAN Ye;LIU Ji-zhi(School of Information and Communication Engineer,University of Electronic Science and Technology of China,Chengdu 611731,China;School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)
出处 《电气电子教学学报》 2021年第1期139-144,共6页 Journal of Electrical and Electronic Education
基金 教育部产学合作协同育人项目(项目编号:201802106084) 国家自然科学基金面上项目(项目编号:61671120)。
关键词 窄带信号 波达方向 压缩感知 narrowband signal direction of arrival(DOA) compressive sensing
  • 相关文献

参考文献3

二级参考文献42

  • 1Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 2Cands E and Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
  • 3Malioutov D, Cetin M, and Willsky A. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010 3022.
  • 4Cevher V Boufounos P, Baraniuk R, et al.. Near-optimal bayesian localization via incoherence and sparsity[C]. Proceedings of the International Conference on Information Processing in Sensor Networks, San Francisco, 2009: 205-216.
  • 5Duarte M. Localization and bearing estimation via structured sparsity models[C]. Proceedings of the IEEEStatistical Signal Processing Workshop, Ann Arbor, 2012: 333-336.
  • 6Kim J, Lee O, and Ye J. Compressive MUSIC: revisiting the link between compressive sensing and array signal processing [J]. 1EEE Transactions on Information Theory, 2012, 58(1): 278-3{)1.
  • 7Lee K, Bresler Y, and Junge M. Subspace methods for joint sparse recovery[J]. IEEE Transactions on Information Theory, 2012, 58(6): 3616-3641.
  • 8Donoho D, Elad M, and Temlyakov V. Stable recovery of sparse overcomplete representations in the presence of noise[J]. [EEE Transactions on Information Theory, 2006, 52(1): 6-18.
  • 9Chi Yue-jie, Scharf L, Pezeshki A, et al.. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2182-2195.
  • 10Herman M and Strohmer T. General deviants: an analysis of perturbations in compressed sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 342-349.

共引文献39

同被引文献8

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部