基于自相关检测法和能量重心法的正弦信号频率估计算法

为了提高淹没在高斯白噪声中的实正弦信号的频率估计精度,提出了一种综合的结合自相关检测法和能量重心法的正弦信号频率估计算法。该算法首先通过多次自相关运算对输入信号进行预处理,可检测出淹没在噪声中的微弱正弦信号,来提高信噪比;然后对信号进行FFT(fast fourier transform)运算可得信号的功率谱,通过搜索最大值谱线的位置可粗估计出信号频率;最后运用离散频谱能量重心法,可精确估计出正弦信号的频率。仿真结果表明本算法在整个频段上频率估计性能比较稳定、频率估计的均方根误差更小,性能优于Rife算法、Quinn算法和能量重心法,并易于硬件实现,具有工程实用价值。

Frequency Estimation Algorithm of Sinusoid Signal Based on Autocorrelation Detection and Energy Centrobaric Correction Method

In order to improve the frequency estimation accuracy of sinusoidal signal in white Gaussian noise , a comprehensive sinusoidal frequency estimation algorithm combined autocorrelation detection with energy centrobaric correction method was proposed. Firstly the weak sinusoidal signal embedded in white Gaussian noise was detected by multiple autocorrelation to improve the signal to noise ratio. Then the power spectrum could be obtained by the Discrete Fourier Transform, and the signal frequency could be roughly estimated by searching the position of the maximum spectral line. Finally the sinusoidal signal frequency was accurately estimated by using energy centrobaric correction method for discrete spectrum. Simulation results showed that frequency estimation performance of this algorithm on the whole frequency band is relatively stable, and the root mean square error(RMSE) of frequency estimation error is smaller relative to rife algorithm, quinn algorithm and energy centrobaric correction method. The algorithm is easily implemented in hardware and also has some practical value for engineering.