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Autocorrelation-based algorithm for single-frequency estimation

机译：基于自相关的单频估计算法

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摘要

A novel iterative algorithm for estimating the frequency of a single complex sinusoid in the complex white Gaussian noise is proposed. Inspired by the works of Fitz, L&R, and the ILP approaches, the algorithm is based on the repeated use of an autocorrelation-based frequency estimator. This approach is different from the iterative linear prediction (ILP) algorithm, yet produces very comparable performance. Like the ILP algorithm, the proposed estimator has the reduced threshold, and its performance is close to that of the ML estimation and uniform across the frequency range of -π to π. In addition, the proposed estimator demonstrates an asymptotic error variance of 0.14 dB above the CRB for N = 1024 and 0.62 dB for N = 48, which is better than that of the ILP algorithm.
机译：提出了一种新颖的迭代算法，用于估计复杂白高斯噪声中单个复杂正弦波的频率。受到Fitz，L＆R和ILP方法工作的启发，该算法基于重复使用基于自相关的频率估算器。这种方法与迭代线性预测（ILP）算法不同，但产生了非常可比的性能。像ILP算法一样，所提出的估计器具有降低的阈值，其性能接近ML估计的性能，并且在-π到π的频率范围内保持一致。另外，所提出的估计器表明，对于N = 1024，CRB上方的渐近误差方差为0.14 dB，对于N = 48，其渐近误差方差为0.62 dB，这优于ILP算法。

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