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.

Acoustic echo cancellation using nonlinear cascade filters

Abstract: The miniaturization of GSM handsets creates nonlinear acoustical echoes between microphones and loudspeakers when the signal level is high (hands-free communication). Several methods including nonlinear cascade filters and a bilinear filter are proposed to compensate these echoes. A bilinear filter is a restricted NARMAX (nonlinear autoregressive moving average with exogenous inputs) filter. We present an evaluation based on the standard ERLE (echo return loss enhancement) measure, between a simple linear adaptive FIR filter and various nonlinear filters. These experiments are carried out first on a simulated communication system, then on experimental signals.

Analytical Approach to Wave Field Reconstruction Filtering in Spatio-Temporal Frequency Domain

Abstract: For transmission of a physical sound field in a large area, it is necessary to transform received signals of a microphone array into driving signals of a loudspeaker array to reproduce the sound field. We propose a method for transforming these signals by using planar or linear arrays of microphones and loudspeakers. A continuous transform equation is analytically derived based on the physical equation of wave propagation in the spatio-temporal frequency domain. By introducing spatial sampling, the uniquely determined transform filter, called a wave field reconstruction filter (WFR filter), is derived. Numerical simulations show that the WFR filter can achieve the same performance as that obtained using the conventional least squares (LS) method. However, since the proposed WFR filter is represented as a spatial convolution, it has many advantages in filter design, filter size, computational cost, and filter stability over the transform filter designed by the LS method.

Fast and Provably Accurate Bilateral Filtering

Abstract: The bilateral filter is a non-linear filter that uses a range filter along with a spatial filter to perform edge-preserving smoothing of images. A direct computation of the bilateral filter requires $O(S)$ operations per pixel, where $S$ is the size of the support of the spatial filter. In this paper, we present a fast and provably accurate algorithm for approximating the bilateral filter when the range kernel is Gaussian. In particular, for box and Gaussian spatial filters, the proposed algorithm can cut down the complexity to $O(1)$ per pixel for any arbitrary $S$ . The algorithm has a simple implementation involving $N+1$ spatial filterings, where $N$ is the approximation order. We give a detailed analysis of the filtering accuracy that can be achieved by the proposed approximation in relation to the target bilateral filter. This allows us to estimate the order $N$ required to obtain a given accuracy. We also present comprehensive numerical results to demonstrate that the proposed algorithm is competitive with the state-of-the-art methods in terms of speed and accuracy.

A Stationary Wavelet-Domain Wiener Filter for Correlated Speckle

Abstract: In this paper, we develop a Wiener-type speckle filter that operates in the stationary wavelet domain. We denote it as the stationary wavelet-domain Wiener (SWW) speckle filter. We assume that both the speckle-free image and the speckle contribution have spatial correlations and utilize well-established models for the power density spectrum of the radar cross section to estimate the autospectra that define the filter. It turns out that the filter is independent of the wavelet-domain scale level, i.e., the filter is the same at all scale levels. The SWW filter works on nonoverlapping blocks in the wavelet domain, which are obtained by a quadtree algorithm. Due to the dyadic support of the wavelet coefficients, a natural smoothing is carried out on the boundaries between neighboring blocks, and no visual boundary effects can be observed. The SWW filter is unbiased and shows good performance in despeckling synthetic aperture radar (SAR) images. It smooths homogeneous areas while preserving textured areas and point scatterers. In contrast to most other speckle filters, the SWW filter requires the SAR data to be given in single-look complex form.

Target Tracking Using a Joint Acoustic Video System

Abstract: In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target's position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter's performance