摘要： 为实现欠定条件下同步组网多跳频信号的盲源分离，提出一种基于平行因子分析模型与子空间投影法的跳频信号分离方法。通过计算跳频信号时延相关矩阵构造三阶张量，将混合矩阵估计问题转化为张量CP分解问题。同时改进用于CP分解的经典最小二乘（ALS）算法，使用直接三线性分解方法粗估加载矩阵作为ALS初始迭代矩阵，在迭代过程中采用标准线搜索加速收敛得到混合矩阵。在此基础上，利用子空间投影法完成跳频信号的盲源分离，并剔除离散噪点进一步优化分离效果。仿真结果表明，该方法能够有效提高混合矩阵估计精度，改善源信号恢复效果。

关键词: 同步组网, 跳频信号, 欠定盲源分离, 平行因子分析, CP分解, 子空间投影

Abstract: To implement blind source separation of frequency-hopping signals in synchronous networking under underdetermined conditions,this paper proposes a frequency-hopping signal separation method based on the parallel factor analysis model and subspace projection method.The method calculates the time-delay correlation matrix of the frequency-hopping signals to construct the third-order tensor,and thus the hybrid matrix estimation problem is transformed into the tensor Canonical-Polyadic(CP) decomposition problem.Meanwhile,the classic Alternating Least Square(ALS) algorithm for CP decomposition is improved.The direct trilinear decomposition method is used to roughly estimate the loading matrix,which then serves as the initial iterative matrix of ALS.During the iterations,the standard linear search is used to accelerate the convergence,and the hybrid matrix is estimated.On this basis,the subspace projection method is used to complete the blind source separation of the frequency-hopping signals,and the separation result is optimized by ruling out the discrete noises.Simulation results show that this method can effectively improve the estimation accuracy of mixed matrix and the recovery effect of source signal.

Key words: synchronous networking, frequency-hopping signal, underdetermined blind source separation, parallel factor analysis, Canonical-Polyadic(CP) decomposition, subspace projection

中图分类号: 

• TP393