Title :
Adaptive algorithm based on least mean p-power error criterion for Fourier analysis in additive noise
Author :
Xiao, Yegui ; Tadokoro, Yoshiaki ; Shida, Katsunori
Author_Institution :
Fac. of Sci. & Eng., Saga Univ., Japan
fDate :
4/1/1999 12:00:00 AM
Abstract :
This abstract presents a novel adaptive algorithm for the estimation of discrete Fourier coefficients (DFC) of sinusoidal and/or quasiperiodic signals in additive noise. The algorithm is derived using a least mean p-power error criterion. It reduces to the conventional LMS algorithm when p takes on 2. It is revealed by both analytical results and extensive simulations that the new algorithm for p=3, 4 generates much improved DFC estimates in moderate and high SNR environments compared with the LMS algorithm, whereas both have similar degrees of complexity. Assuming the Gaussian property of the estimation error, the proposed algorithm, including the LMS algorithm, is analyzed in detail. Elegant dynamic equations and closed-form noise misadjustment expressions are derived and clarified
Keywords :
Fourier analysis; Gaussian processes; adaptive estimation; adaptive signal processing; discrete Fourier transforms; error analysis; least mean squares methods; noise; Fourier analysis; Gaussian property; LMS algorithm; SNR; adaptive algorithm; additive noise; closed-form noise misadjustment expressions; complexity; discrete Fourier coefficients estimation; dynamic equations; estimation error; least mean p-power error criterion; quasiperiodic signals; simulations; sinusoidal signals; Adaptive algorithm; Additive noise; Algorithm design and analysis; Analytical models; Digital-to-frequency converters; Equations; Estimation error; Least squares approximation; Signal to noise ratio; Working environment noise;
Journal_Title :
Signal Processing, IEEE Transactions on
