【摘要】 为解决α噪声背景下的谐波恢复问题,提出了归一化循环相关结合多重信号分类算法。该算法包含两种多重信号分类算法(MUSIC:Multiple Signal Classification):样本空间MUSIC算法和特征空间MUSIC算法。这两种MUSIC算法充分利用信号子空间和噪声子空间,在空域内做谱峰搜索以求取谐波频率。该算法不仅能估算谐波信号频率,同时也能提高谐波估计的精度。计算机仿真结果表明,使用这两种算法可完成谐波信号频率有效估算,而且效果比原有分数低阶矩及其派生的分数低阶统计量更优,且有效地解决了非整数算子造成的相位扭曲问题,应用前景广泛。更多还原

【Abstract】 In order to solve the harmonic recovery problem in the background of α noise,a normalized cyclic correlation combined with multiple signal classification algorithm is proposed. It consists of two proposed multiple signal classification algorithms( MUSIC: Multiple Signal Classification) : sample space MUSIC algorithm and feature space MUSIC algorithm. The two MUSIC algorithms make full use of the signal subspace and the noise subspace to perform spectral peak search in the spatial domain to find the harmonic frequeacy.This algorithm can estimate the harmonic signal frequency and improve the accuracy of harmonic estimation.The computer simulation results show that the two algorithms can effectively estimate the frequency of the harmonic signal,and the effect is better than the original fractional lower moment and its derived fractional loworder statistic,and effectively solve the non-integer operator. The resulting phase distortion problem has broad application prospects.更多还原