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.

Entrainment avoidance with an auto regressive filter

Abstract: A method of signal processing an input signal in a hearing aid to avoid entrainment, the hearing aid including a receiver and a microphone, the method comprising using an adaptive filter to measure an acoustic feedback path from the receiver to the microphone and adjusting an adaptation rate of the adaptive filter using an output from a filter having an autoregressive portion, the output derived at least in part from a ratio of a predictive estimate of the input signal to a difference of the predictive estimate and the input signal.

Data adaptive rank-shaping methods for solving least squares problems

Abstract: There are two types of problems in the theory of least squares signal processing: parameter estimation and signal extraction. Parameter estimation is called "inversion" and signal extraction is called "filtering". In this paper, we present a unified theory of rank shaping for solving overdetermined and underdetermined versions of these problems. We develop several data-dependent rank-shaping methods and evaluate their performance. Our key result is a data-adaptive Wiener filter that automatically adjusts its gains to accommodate realizations that are a priori unlikely. The adaptive filter dramatically outperforms the Wiener filter on a typical realizations and just slightly under-performs it on typical realizations. This is the most one can hope for in a data-adaptive filter. >

A unified method to energy-to-peak filter design for networked Markov switched singular systems over a finite-time interval

Abstract: For a class of networked singular Markov switched discrete-time systems, this work investigates the finite-time energy-to-peak filtering problem. The system modes information is transmitted though an unreliable communication link in the systems under consideration, where the packet dropout phenomenon, modes information available to the filter and asynchronous phenomenon between filter modes and system modes are randomly occurring with a certain probability and described by some Bernoulli distributed white sequence variables. The objective is focused on designing a unified filter, which covers mode-independent filter, asynchronous filter and mode-dependent filter, so that the estimation error system is finite-time bounded while meets a fixed energy-to-peak performance requirement in the presence of those stochastic phenomena. By employing a probability-dependent Lyapunov–Krasovskii function, some sufficient criteria are established to make sure that there is a feasible solution to the addressed problem. Moreover, with the help of a novel simple matrix decoupling approach, the filter gains are obtained. In the end, we employ a numerical example with simulation to show the serviceability of the presented method.

Adaptive transmit equalizer

Abstract: An adaptive transmit equalizer for equalizing digital symbols to be transmitted over a communications channel includes a transmit filter having an associated register for storing at least one filter coefficient. A tuning engine generates performance metric values based on digital symbols received through the communications channel. The tuning engine is configured to identify at least one filter coefficient based on the performance metric values using a simplex algorithm. The tuning engine is configured to communicate the at least one updated filter coefficient to the transmit filter for storage in the register.