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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: The continuous-time LMS (least-mean squares) algorithm is described by a set of simultaneous first-order equations and the adaptive gain is shown to be unbounded theoretically.
Abstract: A continuous-time analog adaptive filter is suggested using the digital prototype. The continuous-time LMS (least-mean squares) algorithm is then described by a set of simultaneous first-order equations. The adaptive gain is shown to be unbounded theoretically. >

35 citations

Journal ArticleDOI
TL;DR: In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas inkernel-based learning theory to outperforms the conventional linear matched filter.
Abstract: In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.

35 citations

Book ChapterDOI
20 Sep 2010
TL;DR: This work proposes an adaptive version of the Linear Minimum Mean Square Error estimator that applies an adaptive filtering kernel that is based on a space-variant estimate of the noise level and a weight consisting of the product of a Gaussian kernel and the diffusion similarity with respect to the central voxel.
Abstract: Measuring the diffusion properties of crossing fibers is very challenging due to the high number of model parameters involved and the intrinsically low SNR of Diffusion Weighted MR Images. Noise filtering aims at suppressing the noise while pertaining the data distribution. We propose an adaptive version of the Linear Minimum Mean Square Error (LMMSE) estimator to achieve this. Our filter applies an adaptive filtering kernel that is based on a space-variant estimate of the noise level and a weight consisting of the product of a Gaussian kernel and the diffusion similarity with respect to the central voxel. The experiments show that the data distribution after filtering is still Rician and that the diffusivity values are estimated with a higher precision while pertaining an equal accuracy. We demonstrate on brain data that our adaptive approach performs better than the initial LMMSE estimator.

35 citations

Proceedings ArticleDOI
12 May 1996
TL;DR: An adaptive analog notch filter is proposed for applications requiring high frequency adaptive signal processing and is capable of rejecting a sinusoid and with a second notch filter section attached, is able to reject a pair of sinusoids.
Abstract: An adaptive analog notch filter is proposed for applications requiring high frequency adaptive signal processing. Based on a log filter circuit topology, this filter design suggests new circuitry for the implementation of analog adaptive filters. Simulation results demonstrate that the filter is capable of rejecting a sinusoid, and with a second notch filter section attached, is capable of rejecting a pair of sinusoids. The circuit design and the adaptation method are described.

35 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a solution for the resulting complex Wiener filter in terms of complex autocorrelation and cross-correlation functions, and an algorithm for the efficient evaluation of the complex filter weights is also available.
Abstract: The ordinary time or space domain Wiener filter is conventionally obtained for real-valued inputs and real-valued desired outputs. Situations exist for which these variables are complex-valued.It is possible to develop a solution for the resulting complex Wiener filter in terms of complex autocorrelation and crosscorrelation functions. An algorithm for the efficient evaluation of the complex filter weights is also available.

35 citations


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