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Hunter Phillips
Hunter Phillips

Fundamentals Of Adaptive Filtering Sayed Pdf 14 _TOP_

Dr. Sayed's research interests span several areas, including adaptive and statistical signal processing, equalization techniques for communications, echo cancellation, wireless location, linear and nonlinear filtering and estimation, interplays between signal processing and control methodologies, and reliable and efficient algorithms for large-scale structured computations.

fundamentals of adaptive filtering sayed pdf 14

Dr. Sayed is the recipient of the 1996 IEEE Donald G. Fink Award for the paper, "A State-Space Approach to Adaptive RLS Filtering". In January of 2001 he was named Fellow of the IEEE for his contributions to adaptive filtering and estimation algorithms.

Review of linear algebra and random processes. Optimal estimation. Linear estimation. Steepest-descent algorithms. Stochastic-gradient algorithms. Least squares and recursive least squares. Kalman filtering. Particle filtering. Blind deconvolution and beamforming. Subspace tracking. Robust adaptive filters. Iterative solvers of large-scale linear systems. Selected emerging topics.

The purpose of adaptive filtering is to estimate the target variable T in some sense by designing a model M to construct a output Y from input X. Under MCCC, we find this mode by maximizing the complex correntropy between T and Y:

However, the center of complex correntropy is always at zero, which is not the best option in the case of non-zero mean noise. Although the maximum corentropy criterion with variable center in [25] and [26] can be suitable for the variable center, they cannot be used for complex domain adaptive filtering. To overcome their defects, this paper proposes the maximum complex correntropy criterion with variable center (MCCC-VC).

The main contributions of this research lie in the following aspects: (1) we define a MCCC-VC and give its probability explanation; (2) based on the MCCC-VC, we propose a novel adaptive filtering algorithm in complex domain by utilizing the gradient descend approach; (3) we give effective and feasible methods to estimate the kernel center and update the kernel width adaptively; (4) we derive the bound for the learning rate, and the theoretical steady-state excess mean square error (EMSE) of the MCCC-VC algorithm, and verify the theoretical analysis by simulations.


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