Journal of Professor & SPaC member

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Last Updated : 03/2016
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  • Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs (vol 49, pg 1270, 2013)

    S Mok, PG Park

    ELECTRONICS LETTERS 50 (3), 233-233

    Abstract A bias-compensated error-modified normalised least-mean-square algorithm is proposed. The proposed algorithm employs nonlinearity to improve robustness against impulsive measurement noise, and introduces an unbiasedness criterion to eliminate the bias due to noisy inputs in an impulsive measurement noise environment. To eliminate the bias properly, a new estimation method for the input noise variance is also derived. Simulations in a system identification context show that the proposed algorithm outperforms the other algorithms because of the improved adaptability to impulsive measurement noise and input noise in the system.
  • Improved criteria on robust stability and H∞ performance for linear systems with interval time-varying delays via new triple integral functionals

    WI Lee, SY Lee, PG Parkk

    Applied Mathematics and Computation 243, 570-577

    Abstract This paper analyzes delay-dependent robust stability and H∞ performance of linear systems with an interval time-varying delay, based on a new Lyapunov–Krasovskii functional containing new triple integral terms. The time derivative of the Lyapunov–Krasovskii functional produces not only the strictly proper rational functions but also the non-strictly proper rational functions of the time-varying delays with first-order denominators. The combinations of the rational functions are directly handled via the Jensen inequality lemma and the lower bound lemma for reciprocal convexity, whereas such combinations were approximated in the literature. The proposed criteria become less conservative with the significantly smaller number of decision variables than the existing criteria, which will be demonstrated by some numerical examples.
  • Second-order reciprocally convex approach to stability of systems with interval time-varying delays

    WI Lee, PG Park

    Applied Mathematics and Computation 229, 245-253

    Abstract Recently, some triple integral terms in the Lyapunov–Krasovskii functional have been introduced in the literature to reduce conservatism in the stability analysis of systems with interval time-varying delays. When we apply the Jensen inequality to partitioned double integral terms in the derivation of LMI conditions, a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters emerges. This paper proposes an efficient method to manipulate such a combination by extending the lower bound lemma. Some numerical examples are given to demonstrate the improvement of the proposed method.
  • Variable matrix-type step-size affine projection algorithm with orthogonalized input vectors

    PG Park, JH Seo, NW Kong

    Signal Processing 98, 135-142

    Abstract In this paper, we propose a variable matrix-type step-size affine projection algorithm (APA) with orthogonalized input vectors. We generate orthogonalized input vectors using the Gram–Schmidt process to implement the weight update equation of the APA using the sum of normalized least mean squares (NLMS)-like updating equations. This method allows us to use individual step sizes corresponding to each NLMS-like equation, which is equivalent to adopting the step size in the form of a diagonal matrix in the APA. We adopt a variable step-size scheme, in which the individual step sizes are determined to minimize the mean square deviation of the APA in order to achieve the fastest convergence on every iteration. Furthermore, because of the weight vector updated successively only along each innovative one among the reused inputs and effect of the regularization absorbed into the derived step size, the algorithm works well even for badly excited input signals. Experimental results show that our proposed algorithm has almost optimal performance in terms of convergence rate and steady-state estimation error, and these results are remarkable especially for badly excited input signals.
  • Variable Step-Size Affine Projection Sign Algorithm

    JW Yoo, JW Shin, PG Park

    IEEE

    Abstract In this paper, we propose a variable matrix-type step-size affine projection algorithm (APA) with orthogonalized input vectors. We generate orthogonalized input vectors using the Gram–Schmidt process to implement the weight update equation of the APA using the sum of normalized least mean squares (NLMS)-like updating equations. This method allows us to use individual step sizes corresponding to each NLMS-like equation, which is equivalent to adopting the step size in the form of a diagonal matrix in the APA. We adopt a variable step-size scheme, in which the individual step sizes are determined to minimize the mean square deviation of the APA in order to achieve the fastest convergence on every iteration. Furthermore, because of the weight vector updated successively only along each innovative one among the reused inputs and effect of the regularization absorbed into the derived step size, the algorithm works well even for badly excited input signals. Experimental results show that our proposed algorithm has almost optimal performance in terms of convergence rate and steady-state estimation error, and these results are remarkable especially for badly excited input signals.
  • A variable step-size affine projection algorithm with a step-size scaler against impulsive measurement noise

    I Song, PG Park

    Signal Processing 96, 321-324

    Abstract This letter proposes a variable step-size (VSS) affine projection algorithm (APA) associated with a step-size scaler to improve the APA’s robustness against impulsive measurement noise. In the proposed VSS APA, the step-size scaler is applied to the equations for updating the step size, which are developed by interpreting the behavior of the mean square deviation (MSD) of the conventional APA. To reduce the computational complexity, we also propose a simplified version of the step-size scaler, which is suitable for application in the APA. Simulations show that the proposed algorithm leads to an excellent transient and steady-state behavior with colored inputs in impulsive-noise environments.
  • Variable Step-Size Affine Projection Sign Algorithm

    JW Yoo, JW Shin, PG Park

    Signal Processing 104, 407-411

    Abstract This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization. Since it is difficult to obtain the value of the MSD accurately, the proposed step size is derived by minimizing the upper bound of the conditional MSD with given input. By assigning the different step size in each band, the filter performance can be improved. Moreover, we suggest the estimation method of the measurement-noise variance in an impulsive-noise environment, because the proposed algorithm needs the measurement-noise variance to calculate the step size. The reset algorithm is also applied for maintaining the filter performance when a system change occurs suddenly. Simulation results demonstrate that the proposed algorithm performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.
  • Variable individual step-size subband adaptive filtering algorithm

    JH Seo, PG Park

    Electronics Letters 50 (3), 177-178

    Abstract A subband adaptive filtering algorithm is proposed which improves its performance by adjusting step sizes. The proposed algorithm derives the individual step sizes for each subband instead of using a common step size for multiple subbands. The derivation of the step sizes is based on the mean-square deviation minimisation in order to achieve the fastest convergence at the instant. Furthermore, the individual step sizes contain the squared norm of the input vector, hence it leads to the regularisation effect that helps the algorithm work well in the case of badly excited input signals. The simulation results show that the proposed algorithm achieves a faster convergence rate and a smaller steady-state estimation error than the existing algorithms.