KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Punchalard, R.Subject: search.filters.subject.Adaptive algorithm

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Linear prediction based adaptive algorithm for a complex sinusoidal frequency estimation
    (2013-06-01)
    Punchalard, R.
    ;
    Wardkein, P.
    The complex direct frequency estimation (CDFE) adaptive algorithm is developed and proposed in this paper. The motivation of this work is obtained from the previous real DFE (RDFE) adaptive algorithm. The methodology of the CDFE is based on the linear prediction property of complex sinusoidal signals. The proposed algorithm is unbiased and computationally efficient. Moreover, it is easy to implement and appropriate for real-time applications. In addition, the convergence behavior is analyzed and the steady-state mean square error (MSE) of the frequency estimate is derived in closed form. Computer simulations are treated to corroborate the theoretical analysis. © 2013 Published by Elsevier GmbH.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.