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.

Edge preserving orientation adaptive filtering

Abstract: In this paper we describe a new stragegy for combining orientation adaptive filtering and edge preserving filtering. The filter adapts to the local orientation and avoids filtering across borders. The local orientation for steering the filter will be estimated in a fixed sized window which never contains two orientation fields. This can be achieved using generalized Kuwahara filtering. This filter selects from a set of fixed sized windows that contain the current pixel, the orientation of the window with the highest anisotropy. We compare out filter strategy with a multi-scale approach. We found that our filter strategy has a lower complexity and yields a constant improvement of the SNR.

Adaptive Gaussian Sum Filter for Nonlinear Bayesian Estimation

Abstract: A nonlinear filter is developed by representing the state probability density function by a finite sum of Gaussian density kernels whose mean and covariance are propagated from one time-step to the next using linear system theory methods such as extended Kalman filter or unscented Kalman filter. The novelty in the proposed method is that the weights of the Gaussian kernels are updated at every time-step, by solving a convex optimization problem posed by requiring the Gaussian sum approximation to satisfy the Fokker-Planck-Kolmogorov equation for continuous-time dynamical systems and the Chapman-Kolmogorov equation for discrete-time dynamical systems. The numerical simulation results show that updating the weights of different mixture components during propagation mode of the filter not only provides us with better state estimates but also with a more accurate state probability density function.

An adaptive noise cancelling arrangement, a noise reduction system and a transceiver

Abstract: Known is a so-called cross-coupled adaptive noise cancelling arrangement utilizing an adaptive noise filter and an adaptive cross-talk filter in a feedback loop for cancelling correlated noise at a primary signal input and reference input. The known cross-coupled ANC does not operate satisfactorily, particularly not for acoustic noise cancellation. This leads to reverberant-like sound singals, in particular in a typical office room with remote noise sources. A cross-coupled adaptive noise cancelling arrangement is proposed having a different configuration giving rise to a better performance. The adaptive cross-talk filter is split into a prefilter section and an adaptive filter section, the sections using different input signals. The prefilter section estimates the desired signal from the input signal of the noise cancelling arrangement, and the adaptive filter section has its input coupled to the output of the noise cancelling arrangement, a delay section being provided between the input and the output of the noise cancelling arrangement. In an embodiment, the prefilter section and the adaptive filter section are separate filters.

A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography

Abstract: This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state's probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter's solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea.