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.

Design and Analysis of Robust Binary Filters in the Context of a Prior Distribution for the States of Nature

Abstract: An optimal binary-image filter estimates an ideal random set by means of an observed random set. A fundamental and practically important question regards the robustness of a designed filter: to what extent does performance degrade when the filter is applied to a different model than the one for which it has been designed? By parameterizing the ideal and observation random sets, one can analyze the robustness of filter design relative to parameter states. Based on a prior distribution for the states, a robustness mesure is defined for each state in terms of how well its optimal filter performs on models for different states. Not only is filter performance on other states taken into account, but so too is the contribution of other states in terms of their mass relative to the prior state distribution. This paper characterizes maximally robust states, derives performance bounds, treats mean robustness (as opposed to robustness by state), introduces a global filter that is applied across all states, particularizes the entire analysis to a sparse noise model for which there are analytic robustness expressions, and proposes a simplified model for determination of robust states from data. Sufficient conditions are given under which the global filter is uniformly more robust than all state-specific optimal filters.

A novel neural network-based adaptive control for a class of uncertain nonlinear systems in strict-feedback form

Abstract: In this paper, a novel robust adaptive tracking control approach is presented for a class of strict-feedback single-input single-output nonlinear systems. By employing radial basis function neural network to account for system uncertainties, the proposed scheme is developed by combining “command filter” and “minimal learning parameter” techniques. The main advantages of the proposed controller are that: (1) the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided; (2) the problem of “dimensionality curse” is solved, and only one adaptive parameter needs to be updated online. These advantages result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, stability analysis shows that uniform ultimate boundedness of the solution of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed scheme.

Geometry transformation-based adaptive in-loop filter

Abstract: Recently, adaptive in-loop filter (ALF) for image/video coding has attracted increasing attention by its proven capability in improving coding performance. ALF is aiming to minimize the mean square error between original samples and decoded samples by using Wiener-based adaptive filter. Samples in a picture are classified into multiple categories and the samples in each category are then filtered with their associated adaptive filter. The filter coefficients may be signaled or inherited to optimize the tradeoff between the mean square error and the overhead. In this paper, a Geometry transformation-based ALF (GALF) scheme is proposed to further improve the performance of ALF, which introduces geometric transformations, such as rotation, diagonal and vertical flip, to be applied to the samples in filter support region depending on the orientation of the gradient of the reconstructed samples before ALF. With the introduction of geometric transformations, more spatial adaptation is supported without excessive signaling of filter coefficients. The experimental results show that GALF outperforms the existing ALF techniques and it has been adopted by the JEM reference software used as the test platform for future video coding technology exploration in JVET.

Efficient approximate implementations of the fast affine projection algorithm using orthogonal transforms

Abstract: The recently-introduced fast affine projection (FAP) adaptive filter has performance rivalling that of the RLS adaptive filter in many situations. However, the sliding-window RLS algorithm embedded within the FAP adaptive filter can be too complex to implement in some cases. We describe an alternate implementation of the FAP adaptive filter that uses orthogonal transforms to approximately calculate the affine projection. Simulations show that the performance of this simplified algorithm is nearly as good as the original algorithm when the transform is chosen properly. In addition, we provide a gradient step size (GSS) method for enhancing the performance of the FAP adaptive filter in nonstationary signal environments.

The combined-maximum principle in problems of estimating the motion parameters of a maneuvering aircraft

Abstract: The synthesis of an optimum tracking filter for a maneuvering aircraft, a problem that is considered an inverse dynamic problem, is studied on the basis of the combined-maximum principle. The filter equations are obtained without the use of the method of invariant immersion. It is shown that the estimates of trajectory parameters that are obtained via application of the new tracking filter possess higher accuracy characteristics than the estimates of the extended Kalman filter and considerably decrease the amount of computations.