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Author: search.filters.author.Anantrasirichai, N.Subject: search.filters.subject.Convergence

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    A new gradient-based algorithm using variable step-size technique and its application [adaptive IIR notch filter]
    (2002-01-01)
    Benjangkaprasert, C.
    ;
    Jorphochaudom, S.
    ;
    Phuvasitkul, S.
    ;
    Anantrasirichai, N.
    In this paper, a new class of gradient-based algorithm by using variable step-size technique for a second-order adaptive IIR notch filter is presented. An adaptation step-size parameter for the algorithm is worked out from the output signal and the gradient signal. The adaptive algorithm is used for detection of a sinusoid with additive white Gaussian noise, impulse noise and cancellation of 50-Hz interference in the recording of electrocardiogram signals. The performance of this algorithm is proved here to give high convergence speed, high impulse noise robustness and good efficiency of 50-Hz interference cancellation. Finally, the results of computer simulation are given to demonstrate the performance of the proposed algorithm for the adaptive IIR notch filter.
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    A robust variable step-size LMS-like algorithm for a second-order adaptive IIR notch filter for frequency detection
    (2001-01-01)
    Punchalard, R.
    ;
    Benjangkaprasert, C.
    ;
    Anantrasirichai, N.
    ;
    Janchitrapongvej, K.
    The best adaptive algorithm requires fast convergence speed, low variance, unbias and low steady-state mean square error (MSE) in both low and high signal-to-noise ratio (SNR) situations. We have proposed a robust variable step-size LMS-like algorithm (VS-LMS-L) for a second-order adaptive IIR notch filter for frequency detection in radar, sonar and communication systems. This algorithm is compared with the conventional LMS-like algorithm called the plain gradient algorithm (PG). The time-varying step-size μ(n) is adjusted by using the square of the time-averaged estimate of autocorrelation of the present output signal y(n) and the past one y(n-1). This technique can reject the effect of the uncorrelated noise sequence on the step-size update, resulting in a small MSE due to the small final μ(n). Moreover, this algorithm can also improve the convergence speed by comparison with the PG at the same MSE value.