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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
Proceedings ArticleDOI
09 May 1995
TL;DR: In this paper, /spl alpha/-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals.
Abstract: A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this paper, /spl alpha/-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such kind of noise is a requirement of many practical problems. Since, direct application of commonly used adaptation techniques fail in these applications, new approaches for adaptive filtering for /spl alpha/-stable random processes are introduced.

40 citations

Journal ArticleDOI
TL;DR: In this article, the authors explore the application of Kalman-Levy filter to handle maneuvering targets and show that the performance of the Kalman filter in the non-maneuvering portion of track is worse than that of a KF.
Abstract: Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (IMM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an IMM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the IMM with Kalman-Levy module over only Kalman-filter-based IMM for realistic maneuvers is shown by simulation results.

40 citations

Patent
27 Oct 1999
TL;DR: In this article, a perceptual weighting device for producing a perceptually weighted signal in response to a wideband signal comprises a signal preemphasis filter, a synthesis filter claculator, and a perceptual weighted filter.
Abstract: A perceptual weighting device for producing a perceptually weighted signal in response to a wideband signal comprises a signal preemphasis filter, a synthesis filter claculator, and a perceptual weighting filter. The signal preemphasis filter enhances high frequency content of the wideband signal to thereby produce a preemphasised signal. The signal preemphasis filter has a transfer function of the form: P(z)=1 - νz-1 wherein ν is a preemphasis factor having a value located between 0 and 1. The synthesis filter calculator is responsive to the preemphasised signal for producing synthesis filter coefficients. Finally, the perceptual weighting filter processes the preemphasised signal in relation to the synthesis filter coefficients to produce the perceptually weighted signal. The perceptual weighting filter has a transfer function, with fixed denominator, of the form: W(z) A (z/η?1?) / (1-η2z?-1?) where 0∫η?2?∫η1 ≤1 and η2 and η1 are weighting control values, whereby weighting of the wideband signal in a format region is substantially decoupled from a spectral tilt of this wideband signal.

40 citations

Journal ArticleDOI
TL;DR: The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L 1 adaptive controller with fixed filter coefficients.
Abstract: No simple way of tuning L1 adaptive controller feedback filter exists.Propose a Fuzzy-logic based approach for on-line tuning of the filter.Particle Swarm Optimization (PSO) is used to optimize the filter.Class of a strictly proper low pass filters with fixed structure is considered.Simulation demonstrate simplicity excellent performance and robustness. L 1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L 1 adaptive controller with fixed filter coefficients.

40 citations

Journal ArticleDOI
TL;DR: It is shown analytically that the proposed robust adaptive control scheme guarantees stability, performance and robustness with respect to unmodeled dynamics and bounded broadband noise disturbances.
Abstract: In recent years, a class of adaptive schemes has been developed for suppressing periodic disturbance signals with unknown frequencies, phases, and amplitudes. The stability and robustness of these schemes with respect to inevitable unmodeled dynamics and noise disturbances in the absence of persistently exciting signals has not been established despite successful simulation results and implementations. The purpose of this technical note is to propose a robust adaptive scheme for rejection of unknown periodic components of the disturbance and analyze its stability and performance properties. First, we consider the ideal case (non-adaptive) when complete information about the characteristics of the disturbance is available. We show that the rejection of periodic terms may lead to amplification of output noise and in some cases lead to a worse output performance. The way to avoid such undesirable noise amplification is to increase the size of the feedback control filter in order to have the flexibility to achieve rejection of the periodic disturbance terms while minimizing the effect of the noise on the output. The increased filter order leads to an over-parameterized scheme where persistence of excitation is no longer possible, and this shortcoming makes the use of robust adaptation essential. With this important insight in mind, the coefficients of the feedback filter whose size is over parameterized are adapted using a robust adaptive law. We show analytically that the proposed robust adaptive control scheme guarantees stability, performance and robustness with respect to unmodeled dynamics and bounded broadband noise disturbances. We use numerical simulations to demonstrate the results.

40 citations


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