季正燕, 陈辉, 张佳佳. 基于向量化的稀疏重构解相干算法[J]. 电波科学学报, 2017, 32(2): 237-243. doi: 10.13443/j.cjors.2017021901
      引用本文: 季正燕, 陈辉, 张佳佳. 基于向量化的稀疏重构解相干算法[J]. 电波科学学报, 2017, 32(2): 237-243. doi: 10.13443/j.cjors.2017021901
      JI Zhengyan, CHEN Hui, ZHANG Jiajia. Sparse reconstruction decoherence algorithm based on vectorization[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(2): 237-243. doi: 10.13443/j.cjors.2017021901
      Citation: JI Zhengyan, CHEN Hui, ZHANG Jiajia. Sparse reconstruction decoherence algorithm based on vectorization[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2017, 32(2): 237-243. doi: 10.13443/j.cjors.2017021901

      基于向量化的稀疏重构解相干算法

      Sparse reconstruction decoherence algorithm based on vectorization

      • 摘要: 针对稀疏重构中正交匹配追踪(Orthogonally Matched Pursuit,OMP)算法解相干问题,利用矢量化的接收数据自相关矩阵,提出一种改进解相干方法——矢量化正交匹配追踪(Vectorized OMP,VO)算法.改进方法只通过矢量化后的一维矢量来重构角度,无需知道信号源的数目,即可降低噪声影响,实现解相干.相对于经典OMP算法,稀疏重构效果更优.理论分析和仿真结果都验证了算法的良好性能.

         

        Abstract: In order to solve the coherent problem of orthogonally matched pursuit (OMP) algorithm in sparse reconstruction, an improved de-coherent method is proposed by using vectorized autocorrelation matrix. The improved method reconstructs the angle only by the vectorized one-dimensional vector, which can reduce the influence of the noise and realize the decoherence without knowing the number of the signal source. Compared with the classical OMP algorithm, the sparse reconstruction effect is better. Theoretical analysis and simulation results verify the good performance of the algorithm.

         

      /

      返回文章
      返回