摘要
用奇异值分解界定信号和噪声子空间的困难之处,在于有效秩的确定。过去常用的办法是采用固定的阈值确定有效秩,尽管其物理意义明显,但由于缺乏自适应性,影响了子空间法在谱线增强中的应用。本文针对这一缺陷,提出一种根据含噪谐波信号的时频分布,用聚类方法确定有效秩的算法。仿真实验的结果表明了这种算法对一类多谱线增强问题非常有效。
A general difficulty of using singular value decomposition(SVD) to split signal and noise subspaces is in the right choice of effective rank. The commonly used method toward this end is to use a fixed threshold. Yet despite its apparent physical significance, the lack oi adaptability has strongly limited the popularity of subspace approach in line enhancement. IE order to surmount this drawback, a cluster method based algorithm for determining the effective rank is proposed in accordance with the a priori information obtained from the time-frequency distribution of the noisy sinusoids. Simulation results show that the methodology advocated is effective for solving a class of multiple line enhancement problems.
基金
国家自然科学基金
国防重点实验室基金
关键词
奇异值分解
子空间
有效秩
谱线增强器
信号处理
Singular value decomposition, Subspace, Effective rank, Line enhancement, Short-time Fourier transform