单频估计的自相关混合算法

A Hybrid Autocorrelation-Based Single Frequency Estimator

  • 摘要: 对于高斯白噪声中单频复正弦信号的频率估计,提出一种基于信号自相关的混合算法。以自相关项相位加权平均方法获得估计初值,再以迭代方式利用自相关项进行频率估计。获得比迭代线性预测算法(ILP)更低的信噪比门限,估计方差在高信噪比时接近Cramér-Rao限,且优于ILP算法。该算法特别适于频率估计范围较窄但要求低信噪比的应用,性能分析和仿真结果说明了该方法的优越性。

     

    Abstract: For single tone complex signal estimator in the presence of additive white Gaussian noise, based on the autocorrelations, we proposes a statistically improved hybrid estimator that outperforms other recently proposed approaches at lower signal-to-noise ratio (SNR). The Cramér-Rao lower bound is closely followed at moderate SNR. The estimator is applicable to problems in applications requiring low SNR and limited frequency range.

     

/

返回文章
返回