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Improved estimation of the frequency of a single sinusoid in noise using a new measurement model

机译:使用新的测量模型改进了噪声噪声频率的估计

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摘要

The problem of estimating the frequency of a single sinusoid in white Gaussian noise is addressed. Results in the literature are based on a model for the observed signal phase that was first proposed in [1]. A new model for the observed signal phase is proposed here that models the observed phase noise more accurately, especially for low signal-to-noise ratios (SNR). Two estimators are designed using these two measurement models. Namely, the Kalman filter and the maximum likelihood estimator. Their mean square estimation error performances are then compared using simulations, and it is shown that the estimators based on the new measurement model perform better at low SNR. The Kalman filter makes use of prior statistical knowledge of the signal and noise models, and thus is able to achieve a lower threshold SNR. In particular, the Kalman filter based on the new measurement model has the lowest threshold SNR.
机译:解决了估计白色高斯噪声中单个正弦曲线频率的问题。结果在文献中基于所观察到的信号阶段的模型,该模型首先提出[1]。此处提出了一种新的观察信号相模型,其更准确地模拟观察到的相位噪声,特别是对于低信噪比(SNR)。使用这两个测量模型设计了两种估算器。即,卡尔曼滤波器和最大似然估计器。然后使用仿真比较其平均方形估计误差性能,并显示了基于新测量模型的估算器在低SNR下表现更好。卡尔曼滤波器利用了信号和噪声模型的现有统计知识,因此能够实现较低的阈值SNR。特别是,基于新测量模型的卡尔曼滤波器具有最低的阈值SNR。

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