期刊文献+

基于压缩感知的自适应数字波束形成算法 被引量:9

Adaptive Digital Beamforming Algorithm Based on Compressed Sensing
下载PDF
导出
摘要 该文根据目标在空间的稀疏性,提出了接收端的基于压缩感知理论的自适应数字波束形成算法。在阵元稀布的情况下,用压缩感知的压缩采样理论,恢复出缺失通道的回波信息,然后用恢复的信号做数字波束形成。该算法所形成的波束具有波束旁瓣低,指向误差小,干扰方向零陷深,而且没有栅瓣等优点,波束性能接近满阵时候的波束性能,而且使用该方法减少的阵元数远远大于其他稀布阵方法减少的阵元数。采用蒙特卡罗方法对该方法进行了性能评估,给出了不同信噪比、不同干噪比、不同快拍情况下的计算结果,仿真结果也验证了该算法的正确性。 A new adaptive digital beamforming in receiving end based on compressed sensing is proposed. In the case of sparse array antenna, receiving signal from absence elements can be reconstructed by using the theory of compressed sensing. Adaptive digital beamforming techniques are then adopted to form antenna beams, whose main lobe is steered to desired direction and nulls are steered to the directions of interferences. Simulation results with Monte Carlo method show that the beam performances of the proposed method are approaching to that of full array antenna, and actual antenna elements can be reduced greatly.
出处 《电子与信息学报》 EI CSCD 北大核心 2013年第2期438-444,共7页 Journal of Electronics & Information Technology
关键词 压缩感知 数字波束形成 稀布阵 多测量欠定系统正则化聚焦求解算法 Compressed sensing Digital beamforming Sparse arrays Regularized M-FOCUSS
  • 相关文献

参考文献18

二级参考文献129

  • 1方广有,佐藤源之.频率步进探地雷达及其在地雷探测中的应用[J].电子学报,2005,33(3):436-439. 被引量:33
  • 2DONOHO D. Compressed sensing[ J]. IEEE Trans. Information Theory,2006,52(4) :1289 -1306.
  • 3E J Candes and T Tao. Near optimal signal recovery from random projections : Universal encoding strategies [ J ]. IEEE Trans. Info. Theory. 2006,52 (12) :5406 - 5425.
  • 4Tro PPJ, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit. Transactions on In formation Theory,2007, 53 (12) :4655 - 4666.
  • 5Cands E, Romberg J, Tao T. Robust uncertainty principles : Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory. 2006, 52 (2) :489 - 509.
  • 6R Baraniuk. Alecture on compressive sensing[ J]. IEEE Signal Processing Magazine. July 2007,24 (4) :118 -121.
  • 7E J Candes and M B Wakin.. An Introduction to Compressive Sampling[ J]. IEEE Signal Processing Magazine. March 2008,25 (2) :21 -30.
  • 8ZOU J, GILBERT A C, STRAUSS M J, et.al. Theoretical and experimental analysis of a randomized algorithm for sparse Fourier transform analysis [ J ]. Journal of Comp - utational Physics,2006,211 (2) : 572 - 595.
  • 9FIGUEIREDO M A T, NOWAK R D, WRIGHT S J. Gradient projection for sparse reconst - ruction : application to compressed sensing and other inverse problems [ J ]. IEEE J- STSP, 2007,1 (4) :586 -598.
  • 10E Candes. Compressive sampling [ A ]. Proceedings of the International Congress of Mathematicians [ C ]. Madrid, Spain, 2006,3 : 1433 - 1452.

共引文献392

同被引文献84

引证文献9

二级引证文献48

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