Journal of Professor & SPaC member

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Last Updated : 03/2016
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  • A Normalized Least Mean Squares Algorithm With a Step-Size Scaler Against Impulsive Measurement Noise

    I Song, PG Park, RW Newcomb

    Circuits and Systems II: Express Briefs, IEEE Transactions on 60 (7), 442-445

    Abstract This brief introduces the concept of a step-size scaler by investigating and modifying the tanh cost function for adaptive filtering with impulsive measurement noise. The step-size scaler instantly scales down the step size of gradient-based adaptive algorithms whenever impulsive measurement noise appears, which eliminates a possibility of updating weight vector estimates based on wrong information due to impulsive noise. The most attractive feature of the step-size scaler is that this is easily applicable to various gradient-based adaptive algorithms. Several representative gradient-based adaptive algorithms are performed without or with the step-size scaler in impulsive-noise environments, which shows the improvement of robustness against impulsive noise.
  • A bias-compensated affine projection algorithm for noisy input data

    SM Jung, NK Kwon, P Park

    Control Conference (ASCC), 2013 9th Asian, 1-5

    Abstract This paper proposes a bias-compensated affine projection algorithm (BC-APA) to eliminate bias due to noisy input data and to reduce the performance degradation due to highly correlated input data. A new affine projection algorithm (new APA) using innovative input data is presented for highly correlated input data. We analyze the bias in this innovative new APA under noisy input data and remove it. To remove the bias, an estimation method for the input noise variance is presented and explained. In simulations, the BC-APA provided both fast convergence rate and small mean square deviation. Based on improved precision to estimate a finite impulse response of an unknown system, the BC-APA can be applied extensively in adaptive signal processing areas.
  • LPV controller design with multiple parameters for the nonlinear RTAC system

    NK Kwon, BY Park, SM Jung, P Park

    Control Conference (ASCC), 2013 9th Asian, 1-6

    Abstract This paper proposes linear parameter varying (LPV) model with multiple parameters (LPV-MP) and statefeedback controller for the nonlinear rotational and translational actuator (RTAC) benchmark problem. First, based on LPV-MP, the conditions used for designing the state-feedback controller are formulated in terms of parameterized linear matrix inequalities (PLMIs) and the state-feedback LPV controller using multiple parameters-dependent Lyapunov function (MPDLF) is designed. Then, PLMI conditions are converted into linear matrix inequalities (LMIs) by using a parameter relaxation technique. The proposed method results in the reduced decision variables and simulation results show good performance of the proposed method.
  • Bias-compensated normalised LMS algorithm with noisy input

    B Kang, J Yoo, P Park

    Electronics Letters 49 (8), 538-539

    Abstract A new bias-compensated normalised least mean square (NLMS) algorithm for parameter estimation with a noisy input is proposed. The algorithm is obtained from an approximated cost function based on the statistical properties of the input noise and involves a condition checking constraint to decide whether the weight coefficient vector must be updated. Simulation results show that the proposed algorithm is more robust and accurate than the conventional method.