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.

Auxiliary Particle Implementation of Probability Hypothesis Density Filter

Abstract: Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The probability hypothesis density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed sequential Monte Carlo (SMC) implementations of the PHD filter. However these implementations are the equivalent of the bootstrap particle filter, and the latter is well known to be inefficient. Drawing on ideas from the auxiliary particle filter (APF), we present an SMC implementation of the PHD filter, which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

Applying an adaptive feed-forward algorithm as a frequency selective filter in a closed loop disk drive servo system in order to compensate for periodic perturbations which otherwise appear in the servo system position error signal

Abstract: A method for applying an adaptive feed-forward waveform correction to a primary servo loop compensation signal in a rotating data storage apparatus. A position error signal is used to determine a fixed feed-forward correction upon initialization or other predetermined conditions. An adaptive feed-forward waveform correction is periodically determined by adding a scaled version of the position error signal to the stored adaptive feed-forward value, and then processing the resulting sum through a frequency selective filter. The stored adaptive feed-forward correction is then updated with this new result. Simultaneously, the updated adaptive feed-forward correction is combined with the fixed feed-forward correction and the position error signal to generate a primary servo loop compensation signal for controlling the read/write head of the data storage apparatus.

Spatial Filtering for Detection of Signals Submerged in Noise

Abstract: Matched filtering is described as a spatial filtering operation. A technique for producing a matched filter, wherein the filter transfer function is modulated onto a spatial carrier and the resulting function is hard-clipped allowing a filter construction of completely opaque and transparent lines, is given. The effect of this nonlinearity on the S/N is shown to be small. The effects of extraneous frequencies in the filter is shown to be negligible if the spatial carrier is sufficiently high. Experimental results are presented showing the detectability of the signal in the presence of various levels of additive noise.

Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean

Abstract: Suppression of impulse noise in images is an important problem in image processing. In this paper, we propose a novel adaptive iterative fuzzy filter for denoising images corrupted by impulse noise. It operates in two stages-detection of noisy pixels with an adaptive fuzzy detector followed by denoising using a weighted mean filter on the “good” pixels in the filter window. Experimental results demonstrate the algorithm to be superior to state-of-the-art filters. The filter is also shown to be robust to very high levels of noise, retrieving meaningful detail at noise levels as high as 97%.