一种迭代频偏估计算法

提出一种适于低信噪比条件下工作的数据辅助型(data-aided)频偏估计算法。计算接收信号自相关函数的辐角，基于最大似然策略合成频偏估计，并通过迭代消除估计模糊。仿真结果表明：迭代算法具有较大的频偏估计范围(估计范围达±40%符号速率)，与M&M算法相比，迭代算法信噪比门限有接近3dB性能改善，其估计性能更接近FFT最大似然算法和克拉美-劳下界(CRLB)，并且计算量有所降低；基于迭代算法的简化版本与迭代线性预测(ILP)算法相比信噪比门限更低，并且降低了计算复杂度。

An Iterative Carrier Frequency Estimation Algorithm

An iterative data-aided algorithm based on maximum likelihood (ML) for carrier frequency estimation under low signal-to-noise ratio (SNR) environment is proposed. Simulation results show that it can achieve about 3dB lower SNR threshold when compared with M&M algorithm. Its estimation range is large, about 40% of the symbol rate and its accuracy is very close to FFT-based maximum likelihood frequency estimation algorithm and the Cramer-Rao lower bound (CRLB). Moreover, the simplified estimator based on proposed algorithm has both lower threshold and less computational complexity when compared with iterative linear prediction (ILP) algorithm.