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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
More filters
Journal ArticleDOI
TL;DR: An adaptive delay-estimation (ADE) algorithm is proposed for the continuous tracking of time-delay which uses an adaptive delay line which is interpolated by a first-order filter.
Abstract: An adaptive delay-estimation (ADE) algorithm is proposed for the continuous tracking of time-delay. The method uses an adaptive delay line which is interpolated by a first-order filter. Two delay-line interpolating filters are considered, each having a single coefficient which is estimated in real time. The first implements linear interpolation, and the second interpolates using a first-order allpass filter. Since the ADE algorithm is derived from recursive Gauss-Newton optimization, it can be viewed as a recursive maximum likelihood (RML) algorithm for time-delay estimation.

52 citations

Journal ArticleDOI
01 Aug 2001
TL;DR: An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed in this article, where instead of using the conventional least-square cost function, a new cost function based on an Mestimator is used to suppress the effect of impulse noise on the filter weights.
Abstract: An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either contaminated gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under gaussian noise alone.

52 citations

Journal ArticleDOI
TL;DR: In this article, an adaptive filter technique for tuning continuous-time integrated filters is presented based on model-matching configuration and tunes both the poles and zeros of the transfer function.
Abstract: An adaptive filter technique for tuning continuous-time integrated filters is presented. This technique is based on the model-matching configuration and tunes both the poles and zeros of the transfer function. Circuit details of an experimental prototype are given. The experimental prototype consists of an integrated third-order filter that is automatically tuned by off-chip circuitry realizing the adaptive tuning system. Both experimental and simulation results are presented to confirm the viability of the proposed approach. >

52 citations

Journal ArticleDOI
TL;DR: In this article, a robust ensemble filtering scheme based on the H∞ filtering theory is proposed, which is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter.
Abstract: A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter.The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are di...

52 citations

PatentDOI
Ikeda Shigeji1
TL;DR: In this article, an adaptive filter for generating a pseudo noise signal, subtracting the pseudo noise signals from a received signal to output an error signal, and sequentially correcting the filter coefficient of the filter in accordance with the error signal is presented.
Abstract: A noise canceler of the present invention is of the type including an adaptive filter for generating a pseudo noise signal, subtracting the pseudo noise signal from a received signal to thereby output an error signal, and sequentially correcting the filter coefficient of the filter in accordance with the error signal. A second adaptive filter produces a second pseudo noise signal and a second error signal. A first and a second power mean circuit each calculates the signal power of the respective signal. A divider performs division with the resulting two kinds of signal power, so that a signal-to-noise power ratio is estimated. A comparator compares the estimated signal-to-noise power ratio and a delayed version of the same and outputs greater one of them as an extended signal-to-noise power ratio. A step size output circuit corrects, based on the extended signal-to-noise power ratio and reference noise signal power output from a power mean circuit, a step size used to adaptively vary the filter coefficient of the first adaptive filter.

52 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202322
202251
202113
202020
201931
201844