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.
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TL;DR: Extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification and Lyapunov method is used to prove that theKalman filter training is stable.
108 citations
TL;DR: In this article, linear programming techniques were used to determine the optimal filter weights for minimizing the peak range sidelobes of a binary phase-coded waveform, and the resulting filter was compared with the filter obtained by use of the least square approximation to the ideal inverse filter.
Abstract: Linear programming techniques are utilized to determine the optimal filter weights for minimizing the peak range sidelobes of a binary phase-coded waveform. The resulting filter is compared with the filter obtained by use of the least square approximation to the ideal inverse filter. For a test case using the 13-element Barker code the linear programming filter is found to have peak sidelobes as much as 5 dB lower than the least squares filter of the same length.
108 citations
TL;DR: In this article, a feedback cancellation system for a hearing aid or the like adapts a first filter in the feedback path that models the quickly varying portion of the hearing aid feedback path, and adapt a second filter that is used either as a reference filter for constrained adaptation or to model more slowly varying portions of the feedback process.
Abstract: A feedback cancellation system for a hearing aid or the like adapts a first filter in the feedback path that models the quickly varying portion of the hearing aid feedback path, and adapts a second filter in the feedback path that is used either as a reference filter for constrained adaptation or to model more slowly varying portions of the feedback path. The second filter is updated only when the hearing aid signals indicate that an accurate estimate of the feedback path can be obtained. Changes in the second filter are then monitored to detect changes in the hearing aid feedback path. The first filter is adaptively updated at least when the condition of the signal indicates that an accurate estimate of physical feedback cannot be made. It may be updated on a continuous or frequent basis.
107 citations
01 Jan 1982
TL;DR: In this paper, an adaptive genetic algorithm for determining the optimum filter coefficients in a recursive adaptive filter is presented, which does not use gradient techniques and thus is appropriate for use in problems where the function to be optimized is non-unimodal or non-quadratic, such as the mean-squared error surface.
Abstract: An adaptive genetic algorithm for determining the optimum filter coefficients in a recursive adaptive filter is presented. The algorithm does not use gradient techniques and thus is appropriate for use in problems where the function to be optimized is non-unimodal or non-quadratic, such as the mean-squared error surface in a recursive adaptive filter. The mechanisms of the algorithm are inspired by adaptive processes observed in nature. After an initial set of possible filters is randomly selected, each filter is mapped to a binary string representation. Selected bit strings are then transformed using the operations of crossover and mutation to build new "generations" of filters. The probability of selecting a particular bit string to modify and/or replicate for the next "generation" is inversely proportional to its estimated mean-squared error value. Hence, the process not only examines new filter coefficient values, but also retains the advances made in previous "generations". Computer simulations of the algorithm's performance on unimodal and bimodal error surfaces are presented.
107 citations
TL;DR: A tractable, convenient algorithm which can be used to predict the first three moments of a distribution is developed by extending the sigma point selection scheme of the unscented transformation to capture the mean, covariance and skew.
Abstract: The dynamics of many physical system are nonlinear and non- symmetric. The motion of a missile, for example, is strongly determined by aerodynamic drag whose magnitude is a function of the square of speed. Conversely, nonlinearity can arise from the coordinate system used, such as spherical coordinates for position. If a filter is applied these types of system, the distribution of its state estimate will be non-symmetric. The most widely used filtering algorithm, the Kalman filter, only utilizes mean and covariance and odes not maintain or exploit the symmetry properties of the distribution. Although the Kalman filter has been successfully applied in many highly nonlinear and non- symmetric system, this has been achieved at the cost of neglecting a potentially rich source of information. In this paper we explore methods for maintaining and utilizing information over and above that provided by means and covariances. Specifically, we extend the Kalman filter paradigm to include the skew and examine the utility of maintaining this information. We develop a tractable, convenient algorithm which can be used to predict the first three moments of a distribution. This is achieved by extending the sigma point selection scheme of the unscented transformation to capture the mean, covariance and skew. The utility of maintaining the skew and using nonlinear update rules is assessed by examining the performance of the new filter against a conventional Kalman filter in a realistic tracking scenario.
105 citations