Topic
Kernel adaptive filter
About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.
Papers published on a yearly basis
Papers
More filters
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TL;DR: The adaptive filter and adaptive noise cancellation are researched deeply and the results prove its performance is better than the use of a fixed filter designed by conventional methods.
Abstract: In practical application, the statistical characteristics of signal and noise are usually unknown or can't have been learned so that we hardly design fix coefficient digital filter. In allusion to this problem, the theory of the adaptive filter and adaptive noise cancellation are researched deeply. According to the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) algorithms realize the design and simulation of adaptive algorithms in noise canceling, and compare and analyze the result then prove the advantage and disadvantage of two algorithms .The adaptive filter with MATLAB are simulated and the results prove its performance is better than the use of a fixed filter designed by conventional methods.
32 citations
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09 Oct 2006TL;DR: This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes by introducing a time adaptive weighting method for updating each sensor data in each iteration.
Abstract: This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes. This is achieved by introducing a time adaptive weighting method for updating each sensor data in each iteration. Adaptive consensus method will improve node convergence rate, average convergence rate and the variance of error over the network. A mathematical formulation of the method according to the adaptive filter theory as well as derivation of the time adaptive weights and convergence conditions are presented. The analytical results are verified by simulation as well.
32 citations
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23 Aug 2002TL;DR: In this article, the spectral content of an input speech signal is determined based on a defined spectral characteristic (e.g., a defined characteristic slope), and a frequency specific filter component of a weighting filter is controlled based on the determination of the spectral contents of speech signal or/and its location in the encoder.
Abstract: A method for preparing a speech signal for encoding comprises determining whether the spectral content of an input speech signal is representative of a defined spectral characteristic (e.g., a defined characteristic slope). A frequency specific filter component of a weighting filter is controlled based on the determination of the spectral content of speech signal or/and its location in the encoder. A core weighting filter component of the weighting filter may be maintained regardless of the spectral content of the speech signal.
32 citations
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TL;DR: A simplified algorithm is presented for computing the gradient in adaptive IIR (infinite impulse response) lattice filters, and it is concluded that the proposed method allows the development of robust adaptation IIR algorithms with a cost proportional to the filter order.
Abstract: A simplified algorithm is presented for computing the gradient in adaptive IIR (infinite impulse response) lattice filters. For a filter with N zeros and N poles, this algorithm requires only order N computations. Several computer simulations were performed to compare the performance of the proposed O(N) formulation and the conventional O(N/sup 2/) formulation, and no difference in their behavior was found. In these simulations the full Hessian and the diagonal Hessian adaptive algorithms were used in a system-identification configuration. It is concluded that the proposed method allows the development of robust adaptive IIR algorithms with a cost proportional to the filter order. >
32 citations
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TL;DR: Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.
Abstract: The problem under consideration is the adaptive reception of a multipath direct-sequence spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive impulsive noise. An SS receiver structure is proposed that consists of a vector of adaptive chip-based Hampel nonlinearities followed by an adaptive auxiliary-vector linear tap-weight filter. The nonlinear receiver front end adapts itself to the unknown prevailing noise environment providing robust performance over a wide range of underlying noise distributions. The adaptive auxiliary-vector linear tap-weight filter allows rapid SS interference suppression with a limited data record. Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.
32 citations