Sundar G. Sankaran

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—Over the last decade, a class of equivalent algorithms that accelerate the convergence of the normalized LMS (NLMS) algorithm, especially for colored inputs, has been discovered independently. The affine projection algorithm (APA) is the earliest and most popular algorithm in this class that inherits its name. The usual APA algorithms update weight(More)
(ABSTRACT) Adaptive filtering techniques are used in a wide range of applications, including echo cancellation, adaptive equalization, adaptive noise cancellation, and adaptive beamforming. The performance of an adaptive filtering algorithm is evaluated based on its convergence rate, misadjustment, computational requirements, and numerical robustness. We(More)
We present the tracking properties of the Normalized LMS and affine projection class of algorithms for a randomly time-varying system under certain simplifying assumptions on the data. An expression is given for the steady-state mean-squared error. The dependence of the steady-state error and of the tracking properties on three user-selectable parameters,(More)
The bias problem associated with equation error based adaptive infinite impulse response (IIR) filtering can be surmounted by imposing a unit-norm constraint on the autoregressive (AR) coefficients. We propose a hyperspherical parameterization to convert the unit-norm-constrained optimization into an unconstrained optimization. We show that the(More)
We modify the off-line system identification procedure proposed by Regalia [4] into an adaptive IIR filtering algorithm based on the stochastic gradient method. The proposed algorithm aims to minimize equation error, recursively, under a unit-norm constraint on the characteristic polynomial instead of the usual monic constraint. The unit-norm constraint(More)
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