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
TL;DR: It is shown that an optimal solution to the problem of eliminating sinusoidal disturbances from data while producing minimal distortion to the underlying data can be found using Kalman filtering theory.
Abstract: This paper is concerned with the problem of eliminating sinusoidal disturbances from data while producing minimal distortion to the underlying data. A particular example of this problem arises in the filtering of helicopter data which are corrupted by sinusoidal disturbances due to rotor motion. It is shown that an optimal solution to the problem can be found using Kalman filtering theory. The properties of the optimal filter are analyzed using recent results on filtering for nonstabilizable systems. These results are then used to motivate a particular near-optimal filter which has enhanced robustness properties relative to the optimal filter. It will also be shown that an identical filter can be derived using recent results on the evaluation of recursive discrete Fourier transforms. This link between time and frequency domain methods leads to a rather complete understanding of the characteristics of the filter. Specific results are presented showing the application of the filter to real helicopter data.
36 citations
TL;DR: A theoretical convergence analysis of the proposed adaptive training method which can switch off the learning when the training error is too small in terms of external disturbance is presented and results show that the proposed algorithm can adapt the training data effectively with different initial kernel width.
36 citations
13 Nov 1994
TL;DR: The value-and-criterion filter structure is introduced, a new framework for designing filters based on mathematical morphology that finds the mean over the "subwindow" of data with the smallest variance within an overall window.
Abstract: We introduce the value-and-criterion filter structure, a new framework for designing filters based on mathematical morphology. The value-and-criterion filter structure is more flexible than the morphological structure, because it allows linear and nonlinear operations other than just the minimum and maximum to be performed on the data. One particular value-and-criterion filter, the mean of least variance (MLV) filter, finds the mean over the "subwindow" of data with the smallest variance within an overall window. The ability of the MLV filter to smooth noise while preserving and enhancing edges and corners is demonstrated. An example application of the MLV filter in improving the contrast of magnetic resonance images is also shown. >
36 citations
01 May 1978
TL;DR: An IIR adaptive filter algorithm developed by Stearns is discussed, in terms of an example that appeared in a recent article, about the approximation of a fixed second-order filter by a first-order adaptive filter, when subjected to a white noise input.
Abstract: The purpose of this communication is to discuss an IIR adaptive filter algorithm developed by Stearns [1], in terms of an example that appeared in a recent article [2]. The example concerns the approximation of a fixed second-order filter by a first-order adaptive filter, when subjected to a white noise input.
36 citations