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.

Adaptive lattice analysis of speech

Abstract: The adaptive lattice has been shown to have fast convergence properties. In this paper we show experimentally that the convergence of the adaptive lattice is fast enough in that its performance cannot be distinguished from that of the optimal adaptive autocorrelation method for applications in speech analysis/synthesis. A generalized window filter is used in the adaptation process. We show that while the human listener prefers window filters with an effective length of about 20 ms , the shape of the window is especially important for better speech quality. The results have implications for other applications of adaptive filtering.

Singular Value Decomposition-based Robust Cubature Kalman Filtering for an Integrated GPS/SINS Navigation System

Abstract: A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system.

An improved method for the design of quadrature mirror filter banks using the Levenberg–Marquardt optimization

Abstract: This paper presents an improved and efficient method for the design of a two-channel quadrature mirror filter (QMF) bank. In the proposed method, the filter bank design problem is formulated as a low-pass prototype filter design problem, whose responses in the passband and stopband are ideal and their filter coefficients value at quadrature frequency is 0.707. A new method is developed for the design of a low-pass prototype filter which minimizes the objective function by optimizing the filter taps weights using the Levenberg–Marquardt method. When compared with other existing algorithms, it significantly reduces peak reconstruction error (PRE), error in passband, stopband and transition band. Several design examples are included to show the increased efficiency and the flexibility of the proposed method over existing methods. An application of this method is considered in the area of subband coding of the ultrasound images.

Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering

Abstract: The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results.