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为提高高斯白噪声背景中正弦信号的频率估计精度,对基于自相关运算的频率估计算法的相关长度m进行了推导,得到了m的优化值与信噪比的关系式.当信噪比较高时,m的最优值为N/3(N为信号采样点数);信噪比较低时,m的最优值为N/2.通过对自相关法及分段FFT(Fast Fourier Transforms)相位差法特性的分析,提出了一种性能更优的频率估计综合算法.Monte Carlo仿真实验表明:新算法吸收了两种算法的优点,克服了其不足,在更大的信噪比范围内具有较高的频率估计精度,且计算量也较小.
In order to improve the frequency estimation accuracy of sinusoidal signal in Gaussian white noise background, the correlation length m of the frequency estimation algorithm based on the autocorrelation operation is deduced, and the relationship between the optimized value of m and the signal to noise ratio is obtained. The optimal value of m is N / 3 (N is the number of signal sampling points), and the optimal value of m is N / 2 when the signal-to-noise ratio is low. Through the analysis of the autocorrelation method and the Fast Fourier Transforms ), This paper proposes a better performance of the frequency estimation synthesis algorithm.Monte Carlo simulation experiments show that: the new algorithm to absorb the advantages of the two algorithms to overcome its shortcomings in a wider range of signal to noise ratio Within the higher frequency estimation accuracy, and the calculation is also smaller.