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

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.