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
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01 Jan 2018TL;DR: This letter derives recursive filter equations that exhibit similar computational complexity when compared to their Kalman filter counterpart—the extended information filter and proposes a filter that employs a purely residual-based modeling of the available information and thus achieves higher modeling flexibility.
Abstract: This letter deals with recursive filtering for dynamic systems where an explicit process model is not easily devisable. Most Bayesian filters assume the availability of such an explicit process model, and thus may require additional assumptions or fail to properly leverage all available information. In contrast, we propose a filter that employs a purely residual-based modeling of the available information and thus achieves higher modeling flexibility. While this letter is related to the descriptor Kalman filter, it also represents a step toward batch optimization and allows the integration of further techniques, such as robust weighting for outlier rejection. We derive recursive filter equations that exhibit similar computational complexity when compared to their Kalman filter counterpart—the extended information filter. The applicability of the proposed approach is experimentally confirmed on two different real mobile robotic state estimation problems.
47 citations
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TL;DR: A decision feedback equalizer containing a feedback filter with both poles and zeros is proposed for high-speed digital communications over the subscriber loop and results show that the pole-zero DFE offers a significant improvement in mean squared error relative to the conventional DFE.
Abstract: A decision feedback equalizer (DFE) containing a feedback filter with both poles and zeros is proposed for high-speed digital communications over the subscriber loop. The feedback filter is composed of a relatively short FIR filter that cancels the initial part of the channel impulse response, which may contain rapid variations due to bridge taps, and a pole-zero, or IIR, filter that cancels the smoothly decaying tail of the impulse response. Modifications of an adaptive IIR algorithm, based on the Steiglitz-McBride (1965) identification scheme, are proposed to adapt the feedback filter. A measured subscriber loop impulse response is used to compare the performance of the adaptive pole-zero DFE, assuming a two-pole feedback filter, with a conventional DFE having the same number of coefficients. Results show that the pole-zero DFE offers a significant improvement in mean squared error relative to the conventional DFE. The speed convergence of the adaptive pole-zero DFE is comparable to that of the conventional DFE using the standard least mean square (LMS) adaptive algorithm. >
47 citations
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TL;DR: In this paper, a generalized least-squares fault detection filter was proposed for both linear time-invariant and time-varying systems, where the objective is to monitor a single fault called the target fault and block other faults which are called nuisance faults.
Abstract: A fault detection and identification algorithm is determined from a generalization of the least-squares derivation of the Kalman filter. The objective of the filter is to monitor a single fault called the target fault and block other faults which are called nuisance faults. The filter is derived from solving a min–max problem with a generalized least-squares cost criterion which explicitly makes the residual sensitive to the target fault, but insensitive to the nuisance faults. It is shown that this filter approximates the properties of the classical fault detection filter such that in the limit where the weighting on the nuisance faults is zero, the generalized least-squares fault detection filter becomes equivalent to the unknown input observer where there exists a reduced-order filter. Filter designs can be obtained for both linear time-invariant and time-varying systems. Copyright © 2000 John Wiley & Sons, Ltd.
47 citations
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TL;DR: A new algorithm has been developed to minimize the objective function by optimizing the filter tap weights by using Marquardt optimization method, which is better than that of the already existing methods.
47 citations
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TL;DR: The purpose of this paper is to determine the linear optimal filtering algorithm for a message generated by noisy observations of a linear dynamic system with state-dependent, stochastic disturbances and shows that one approximation reduces to thelinear optimal filter.
Abstract: The purpose of this paper is to determine the linear optimal filtering algorithm for a message generated by noisy observations of a linear dynamic system with state-dependent, stochastic disturbances. These disturbances can be considered as stochastic parameter variations. As a consequence of the state-dependent noiso the message process is non-Gaussian. Hence the filter obtained by solving the Wiener-Hopf equation is only the optimal linear operation on the data. The optimal filter is non-linear. Unfortunately the dynamical equations for optimal nonlinear filtering can only be solved approximately. We show that one approximation reduces to the linear optimal filter. As an application we determine the linear optimal filter for a second-order system. This example provides us with a comparison of the performance of the linear optimal filter with a filter designed neglecting the presence of the state-dependent disturbances.
47 citations