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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
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Patent
01 Dec 2011
TL;DR: In this paper, a non-linear adaptive scheme for transmit out of band emission cancellation is proposed, which performs the steps of: extracting the I and Q samples from a modulator output, inputting the I/Q samples to a nonlinear filter, applying weights to the nonlinear filters outputs, combining the filters outputs to generate a broadband emission estimate, selecting a portion of a transmit emission in a desired portion of the receive band, subtracting an output of the non linear filters from a composite signal, and feeding back a residual error to the filters.
Abstract: A method and apparatus for a non-linear adaptive scheme for transmit out of band emission cancellation is provided. Embodiments disclosed herein provide a method for removing unwanted transmitter emissions from a composite received signal. The method performs the steps of: extracting the I and Q samples from a modulator output; inputting the I and Q samples to a non-linear filter; applying weights to the non-linear filter outputs, combining the non-linear filter outputs to generate a broadband emission estimate; selecting a portion of a transmit emission in a desired portion of a receive band; subtracting an output of the non-linear filter from a composite signal; and feeding back a residual error to the non-linear filter; adapting the non-linear filter iteratively.

52 citations

Journal ArticleDOI
TL;DR: In this paper, a novel interpretation of the detection filter in the context of an eigenstructure assignment problem is presented, which is equivalent to the combination of two decoupled observers, with the advantage that theories already developed for conventional observers can be utilized as design aids.
Abstract: A novel interpretation is presented of the detection filter in the context of an eigenstructure assignment problem. The detection filter is shown to be equivalent to the combination of two decoupled observers, with the advantage that theories already developed for conventional observers can be utilized as design aids. Detection space and detection order are re-defined accordingly, and it is shown that of the two observers that constitute a detection filter, the one associated with the detection space is a single-output system. This fact is important since it explains important properties of the fault detection filter in a very uncomplicated fashion, leading to a clear interpretation of the freedom available in designing the filter; in particular, it is proven that the detection filter gains are unique, given the eigenvalues of the detection space. Further, the formulation leads to a closed-form expression for the detection filter, and is therefore well-suited to the development of simple design algorithms.

52 citations

Journal ArticleDOI
TL;DR: A 2D recursive low-pass filter with adaptive coefficients for restoring images degraded by Gaussian noise is proposed and can easily be extended so that simultaneous noise removal and edge enhancement is possible.
Abstract: A 2D recursive low-pass filter with adaptive coefficients for restoring images degraded by Gaussian noise is proposed. Some of the ideas developed are also submitted for nonGaussian noise. The adaptation is performed with respect to three local image features-edges, spots, and flat regions-for which detectors are developed by extending some existing methods. It is demonstrated that the filter can easily be extended so that simultaneous noise removal and edge enhancement is possible. A comparison with other approaches is made. Some examples illustrate the performance of the filter. >

52 citations

Patent
Osamu Hoshuyama1
01 Sep 1995
TL;DR: In this article, a spatial beamforming filter is connected to a sensor array for respectively filtering and summing array signals to produce a first filter output containing a target signal that arrives in a specified direction.
Abstract: In an adaptive array beamformer, a spatial beamforming filter is connected to a sensor array for respectively filtering and summing array signals to produce a first filter output containing a target signal that arrives in a specified direction. First adaptive filters provide transversal-filtering the first filter output to produce a second filter output not containing the target signal, using a first error signal by restraining their tap weight coefficients. The array signals are further coupled to subtractors. Each subtractor detects a difference between the second filter output of the corresponding first adaptive filter and the corresponding sensor signal to derive the first error signal. Second adaptive filters provide transversal-filtering the first error signals of the subtractors to produce third filter outputs, using a second error signal, by restraining their tap weight coefficients. The third filter outputs are summed and subtracted from the first filter output to produce an output of the beamformer, which is supplied as the second error signal to the second adaptive filters

52 citations

Journal ArticleDOI
TL;DR: This work has developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky–Golay filter and its iterative relative, the Kolmogorov–Zurbenko filter that requires no parameter specification or parameter optimization.
Abstract: The automated processing of data from high-throughput and real-time collection procedures is becoming a pressing problem. Currently the focus is shifting to automated smoothing techniques where, unlike background subtraction techniques, very few methods exist. We have developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky–Golay filter and its iterative relative, the Kolmogorov–Zurbenko filter. A crucial difference, however, between these filters and our implementation is that our fully automated smoothing filter requires no parameter specification or parameter optimization. Results are comparable to, or better than, Savitzky–Golay filters with optimized parameters and superior to the automated iterative median filter. Our approach, because it is based on the highly familiar moving average concept, is intuitive, fast, and straightforward to implement and should therefore be of immediate and considerable practical use in a wide variety of spectroscopy applications.

52 citations


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