摘要： 变速跳频信号高跳速和跳速多变的特性使源信号分离难度加大，采用传统基于稀疏分量分析的欠定盲源分离算法无法得到高精度恢复信号。针对该问题，提出一种改进的欠定变速跳频信号盲分离算法。根据变速跳频信号时频域稀疏性不强的特点，通过自适应确定噪声阈值和分解特征值改进时频单源点检测算法，以提高混合矩阵估计精度，同时将聚类与稀疏重构思想应用到源信号分离中，得到稀疏性良好的信号。实验结果表明，该算法可使恢复信号与源信号的相似度达到90%，混合矩阵估计精度较传统单源点检测算法得到有效提高。

关键词: 欠定盲分离, 变速跳频信号, 时频检测, 稀疏分量分析, 稀疏重构

Abstract: The characteristics of high hop rate and variable hop speed of Variable Speed Frequency Hopping(VSFH) signals make the separation of source signals more difficult.Existing underdetermined blind source separation algorithms based on Sparse Component Analysis(SCA) cannot obtain high-precision recovered signals.To solve this problem,this paper proposes an improved blind source separation algorithm for underdetermined VSFH signals.According to the weak sparsity of VSFH signals in the time frequency domain,the noise threshold is adaptively determined,and the time frequency single source point detection algorithm is improved by the noise threshold and decomposition eigenvalue to increase the estimation accuracy of the hybrid matrix.At the same time,the idea of clustering and sparse reconstruction is applied to source signal separation in order to get the signals with good sparsity.Experimental results show that the proposed algorithm can achieve 90% similarity between the recovered signal and the source signal,and the estimation accuracy of the obtained hybrid matrix is improved effectively compared with those of the traditional single source point detection algorithm.

Key words: underdetermined blind separation, Variable Speed Frequency Hopping(VSFH)signal, time frequency detection, Sparse Component Analysis(SCA), sparse reconstruction

中图分类号: 

• TN911