期刊文献+

基于无网格压缩感知的DOA估计算法 被引量:5

DOA Estimating Algorithm Based on Grid-less Compressive Sensing
下载PDF
导出
摘要 应用传统的压缩感知理论对天线阵列信号的波达方向(Direction-of-arrival,DOA)进行估计,存在基的失配问题。基于交替方向乘子法(Alternative Direction Method of Multiplier,ADMM)的无网格压缩感知(Grid-less Compressive Sensing)技术能够解决该问题,但仍存在收敛速度慢的缺陷。针对该缺陷,提出带自适应惩罚项的ADMM(ADMM with adaptive penalty,AP-ADMM)算法,即根据输入信号的噪声功率,自适应地选择惩罚项的初始值;同时在算法迭代求解的过程中,自适应地对目标函数的惩罚项进行调整。与传统算法相比,在保证收敛精度和DOA的恢复成功概率的条件下,带自适应惩罚项的ADMM算法收敛速率明显加快。仿真结果验证了新算法的有效性。 The basis mismatch is existing in the DOA estimation problem by traditional compressive sensing theory.Applying the grid-less compressive sensing technology based on the ADMM algorithm is a wonderful solution,but the convergence rate of the traditional ADMM algorithm was low.To solve this problem,the AP-ADMM algorithm was proposed in this paper.According to the power of the input signals,the AP-ADMM algorithm is able to choose the original numerical value of the penalty adaptively.In addition,the proposed algorithm converges with the ite-rating adaptive penalty.The convergence rate of the proposed algorithm is much higher than the traditional ADMM algorithm.Meanwhile,the accuracy and the probability of successful restoration of the proposed algorithm are approximate with the the traditional ADMM algorithm.The simulation results demonstrate the efficiency of the proposed algorithm.
出处 《计算机科学》 CSCD 北大核心 2017年第10期99-102,133,共5页 Computer Science
基金 国家自然科学基金(61571463 61371124 61272487 61472445 61201217)资助
关键词 波达方向估计 无网格压缩感知 带自适应惩罚项的ADMM算法 DOA , Gr id- less comp re s s iv e s e n s in g, AP-ADMM a lg o r i th m
  • 相关文献

参考文献1

二级参考文献72

  • 1Baraniuk R G. Compressive sensing[lecture notes]. IEEE Signal Processing Magazine, 2007, 24(4):118-121. 被引量:1
  • 2Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2):21-30. 被引量:1
  • 3Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. 被引量:1
  • 4Theodoridis S, Kopsinis Y, Slavakis K. Sparsity-aware learning and compressed sensing:an overview. Academic Press Library in Signal Processing. New York:Academic Press, 2012. 1271-1377. 被引量:1
  • 5Davenport M A, Boufounos P T, Wakin M B, Baraniuk R G. Signal processing with compressive measurements. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2):445-460. 被引量:1
  • 6Ramasamy D, Venkateswaran S, Madhow U. Compressive parameter estimation in AWGN. IEEE Transactions on Signal Processing, 2014, 62(8):2012-2027. 被引量:1
  • 7Chen X S, Zhang X W, Yang J B, Sun M, Yang W W. Cramer-rao bounds for compressive frequency estimation. IEICE Transactions on Fundamentals of Electronics, Communications & Computer Sciences, 2015, 98(3):874-877. 被引量:1
  • 8Huang H, Misra S, Tang W, Barani H, Al-Azzawi H. Applications of compressed sensing in communications networks[Online], available:http://arxiv.org/abs/1305.3002, May 14, 2013. 被引量:1
  • 9Willett R M, Marcia R F, Nichols J M. Compressed sensing for practical optical imaging systems:a tutorial. Optical Engineering, 2011, 50(7):072601. 被引量:1
  • 10Mishali M, Eldar Y C. Wideband spectrum sensing at sub-Nyquist rates[applications corner]. IEEE Signal Processing Magazine, 2011, 28(4):102-135. 被引量:1

共引文献12

同被引文献34

引证文献5

二级引证文献13

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