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.

Robust Huber adaptive filter

Abstract: Classical filtering methods are not optimal when the statistics of the signals violate the underlying assumptions behind the theoretical development. Most of the classical filtering theory like least-squares filtering assumes Gaussianity as its underlying distribution. We present a new adaptive filter that is optimal in the presence of Gaussian noise and robust to outliers. This novel robust adaptive filter minimizes the Huber objective function. An estimator based on the Huber objective function behaves as an L/sub 1/ norm estimator for large residual errors and as an L/sub 2/ norm estimator for small residual errors. Simulation results show the improved performance of the Huber adaptive filter (configured as a line enhancer) over various nonlinear filters in the presence of impulsive noise and Gaussian noise.

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

Abstract: The field of brain–machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100–200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Multiplication-Free One-Bit Transform for Low-Complexity Block-Based Motion Estimation

Abstract: A multiplication-free one-bit transform (1BT) for low-complexity block-based motion estimation is presented in this letter. A novel filter kernel is utilized to construct the 1BT of image frames using addition and shift operations only. It is shown that the proposed approach provides the same motion estimation accuracy at macro-block level and even better accuracy for smaller block sizes compared to previously proposed 1BT methods. Because the proposed 1BT approach does not require multiplication operations, it can be implemented in integer arithmetic using addition and shifts only, reducing the computational complexity, processing time, as well as power consumption

An adaptive filter for synchronous extraction of harmonics and distortions

Abstract: This paper provides an adaptive filter for synchronous detection and extraction of harmonics. The filter can be used as integral part of the control system of a power electronic apparatus (e.g., STATCOM, APF, and UPFC) to generate the desired control signals. Stability and convergence analyses of the adaptive filter are presented based on the dynamical systems theory. Performance of the filter is verified as a means for reference signal generation in a shunt active power filter.

Carrier phase recovery for an adaptive equalizer

Abstract: A method and apparatus are provided for adaptively equalizing data signals in a communications receiver. An unequalized data signal is demodulated. The demodulated data signal is filtered in an adaptive equalizer (60) that initially updates adaptive filter coefficients using error signals derived from a first algorithm. A carrier lock signal is generated (62, 78) when a phase error of a filtered signal output from the adaptive equalizer reaches a threshold value. The adaptive filter coefficients are updated (74) using error signals derived from a second algorithm instead of the first algorithm in response to the carrier lock signal (72). The first algorithm is a self-recovering equalization algorithm such as the Constant Modulus Algorithm. The second algorithm can be a decision directed algorithm. Carrier phase is recovered without the use of a phase rotator or phase de-rotator, by locating the adaptive equalizer inside of the carrier recovery loop (56). The invention is particularly adapted for use in the recovery of multilevel amplitude modulated data, such as QAM data.