Title :
Frequency estimation of non-stationary signals using complex H∞ filter
Author :
Sahoo, H.K. ; Dash, P.K. ; Rath, N.P.
Author_Institution :
Dept. of Electron. & Telecommun., IIIT, Bhubaneswar, India
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.
Keywords :
Hilbert transforms; Kalman filters; frequency estimation; mean square error methods; state-space methods; white noise; Hilbert transform; complex H∞ filter; extended complex Kalman filter; mean square error; noise rejection; nonstationary signal frequency estimation; sinusoidal signal frequency estimation; state space modeling; time varying frequency; white noise; Filtering algorithms; Frequency estimation; Frequency modulation; Mathematical model; Noise measurement; Signal to noise ratio; Time frequency analysis; Complex H∞Filter; Extended Complex Kalman Filter (ECKF); Hilbert Transform; State Space Modeling; White Gaussian Noise;
Conference_Titel :
Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE
Conference_Location :
Trivandrum
Print_ISBN :
978-1-4244-9478-1
DOI :
10.1109/RAICS.2011.6069380
