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.

A Filter Bank and a Self-Tuning Adaptive Filter for the Harmonic and Interharmonic Estimation in Power Signals

Abstract: We present a method that combines a filter bank (FB) system with adaptive filtering to estimate parameters describing the harmonics and the interharmonics present in power signals. The proposed method decomposes the input power signal using an FB system that is a modular binary tree structure with the fundamental FBs arranged successively in each stage. The fundamental FB is designed to separate the odd and even harmonics to reduce spectral leakage. An adaptive filter is used to improve the accuracy of parameter estimation for each decomposed harmonic. Parameters describing the interharmonic components are estimated from the error signal of the adaptive filter, which is self-tuning. The estimation of the amplitude and frequency of each of the harmonic and interharmonic components is done recursively. Computer simulations were performed on synthesized signals to assess the performance of the method.

LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering

Abstract: In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Adaptive finite impulse response filtering method and apparatus

Abstract: The present invention provides a signal processing method and apparatus including a digital finite impulse response filter (53) wherein the error caused by slow asymptotic convergence of the filter's adaptive coefficients is reduced. According to one embodiment of the present invention, a first input signal (x(n)) is provided to an adaptive FIR filter (53) comprising a coefficient calculator (56), an FIR filter (54) and a summation device (62). The filter also receives a second input signal (y(n)), which may, for example, be the echo of the first input signal. The first input signal and the second input signal have a first bandwidth (by 50,58). The adaptive FIR filter provides an output signal to a filter (66), which filters the output signal to a second bandwidth which is less than the first bandwidth. The processed signal will have less error caused by the slow asymptotic convergence of the filter (53) than previously possible because the error-concentrated frequency components near the band edge of the first frequency band have been filtered or removed.

Adaptive reduced-rank residual correlation algorithms for DS-CDMA interference suppression

Abstract: A class of adaptive algorithms for DS-CDMA interference suppression is presented based on the multistage Wiener filter introduced by Goldstein and Reed (see IEEE Trans. Inform. Theory, vol. 44, no.7, 1998). Unlike the principal-components method for reduced-rank filtering this method performs well when the subspace spanned by the filter is less than the dimension of the signal subspace. We present block and recursive algorithms for estimating the filter parameters, which do not require matrix inversion or an eigen-decomposition The algorithm performance in the context of a heavily loaded DS-CDMA system is characterized via computer simulation.

The interacting multiple model algorithm for accurate state estimation of maneuvering targets

Abstract: Accurate state estimation of targets with changing dynamics can be achieved through the use of multiple filter models. The interacting multiple model (IMM) algorithm provides a structure to efficiently manage multiple filter models. Design of an IMM requires selection of the number and type of filter models and selection of each of the individual filter parameters. In this article the results for five filter models on 10 target trajectory segments are discussed and compared. The complexity of the filter models increases from a single constant velocity model to a three-model IMM filter. The results show that the overall performance of the state estimates, for most targets, improves as the complexity of the filter models increases. Selection of IMM filter parameters is addressed and results are provided to show that performance of the IMM appears to be relatively insensitive to large changes in filter parameters. The performance of an IMM is primarily determined by the selection of the component filter models.