在研究Rife和Quinn算法性能的基础上，针对传统频偏估计算法精度易波动以及抗噪能力弱的问题，提出了一种改进型算法。该算法结合了Rife算法在频偏因子较大时的精度优势和Quinn算法的稳定性，并利用添加权重系数的多谱线插值去克服实际频率接近量化频点时产生的修正方向误判问题。实验结果表明：所提算法在较低的信噪比下仍然能保持较高的频率估计精度，相较于其他同类型算法整体性能更稳定、更接近克拉美-罗下界。最后，通过FPGA平台部署算法，并将结果与实际信号频率进行比较和分析，其均方根误差最大在16 Hz左右。

Based on the study of the performance of Rife and Quinn algorithms, an improved algorithm is proposed to solve the problems of the fluctuating accuracy and weak anti-noise ability in traditional frequency offset estimation algorithm. This algorithm combines the precision advantage of the Rife algorithm when dealing with large frequency offset factors with the stability of the Quinn algorithm. It also employs multi-spectral-line interpolation with added weighting coefficients to overcome the problem of misjudging the correction direction when the actual frequency is close to the quantized frequency points. Experimental results demonstrate that the proposed algorithm can maintain high frequency estimation accuracy even under low signal-to-noise ratio (SNR) conditions, exhibiting overall more stable performance and closer approximation to the Cramér-Rao lower bound (CRLB) compared to other similar algorithms. Finally, the algorithm was deployed on an FPGA platform, and the results were compared and analyzed with the actual signal frequency, revealing a maximum root mean square error (RMSE) of approximately 16  Hz.