Frequency estimation of non-stationary signals using complex H∞ filter

H. K. Sahoo, P. Dash, N. P. Rath
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Abstract

A novel estimator is proposed for estimating the frequency of a sinusoidal signal from measurements corrupted by white noise. This estimator is known as Complex H∞ filter which is applied to a noisy sinusoidal model. State Space modeling with two and three states is used for estimation of frequency in presence of white noise. Various simulation results for time varying frequency of the signal reveal significant improvement in noise rejection and accuracy. Comparison in performance between two and three states modeling is presented in terms of mean square error (MSE) under different SNR conditions reveal that two states modeling based on Hilbert transform performs better than three states modeling in a high noisy condition. Frequency estimation performance of the proposed filter is also being compared with Extended Complex Kalman Filter (ECKF) under same noisy condition in some simulation results.
基于复H∞滤波器的非平稳信号频率估计
提出了一种新的估计器,用于估计受白噪声干扰的正弦信号的频率。这种估计器被称为复H∞滤波器,它被应用于一个有噪声的正弦模型。利用二态和三态状态空间模型对白噪声下的频率进行估计。各种时变频率信号的仿真结果表明,该方法在抑制噪声和精度方面有显著提高。从均方误差(MSE)角度比较了不同信噪比条件下二态和三态建模的性能,结果表明,在高噪声条件下,基于Hilbert变换的二态建模优于三态建模。在相同噪声条件下,将该滤波器与扩展复卡尔曼滤波器(ECKF)的频率估计性能进行了比较。
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