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A new class of nonlinear filters-neural filters
IEEE Transactions on Signal Processing, 1993A class of nonlinear filters based on threshold decomposition and neural networks is defined. It is shown that these neural filters include all filters defined either by continuous functions, such as linear finite impulse response (FIR) filters, or by Boolean functions, such as generalized stack filters.
Lin Yin, Jaakko Astola, Yrjö Neuvo
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Nonlinear multiscale filtering
IEEE Signal Processing Magazine, 2002In this article, we give an overview of scale-spaces and their application to noise suppression and segmentation of 1-D signals and 2-D images. Several prototypical problems serve as our motivation. We review several scale-spaces (linear Gaussian, Perona-Malik, and SIDE-stabilized inverse diffusion equation) and discuss their advantages and ...
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Gaussian filter for nonlinear filtering problems
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the proposed filter.
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1967
A formal survey of nonlinear filtering with emphasis on some ad hoc truncation schemes.
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A formal survey of nonlinear filtering with emphasis on some ad hoc truncation schemes.
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Particle filter with Lamarckian inheritance for nonlinear filtering
2016 IEEE Congress on Evolutionary Computation (CEC), 2016The particle filter (PF) offers significant advantages over other nonlinear filters for non-Gaussian systems. However, it suffers from particle degeneracy and impoverishment, which can lead to deteriorated estimation performance. Through analyzing the filtering and Lamarckian evolution processes in this paper, we develop a Lamarckian PF (LPF), based on
Lin Li 0048 +3 more
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On Nonlinear Modular Neural Filters
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006An assessment of the performance of the pipelined recurrent neural network (PRNN) is provided from two aspects, a quantitative one based on the prediction gain and a qualitative one based on examining the changes in the nature of the processed signal.
Mo Chen 0006 +3 more
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Particle flow for nonlinear filters
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011We have solved the well known and important problem of particle degeneracy for particle filters. Our filter is roughly seven orders of magnitude faster than standard particle filters for the same estimation accuracy. The new filter is four orders of magnitude faster per particle, and it requires roughly three orders of magnitude fewer particles to ...
Fred Daum, Jim Huang
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Nonlinear filtering with particle filters
2014Convective phenomena in the atmosphere, such as convective storms, are characterized by very fast, intermittent and seemingly stochastic processes. They are thus difficult to predict with Numerical Weather Prediction (NWP) models, and difficult to estimate with data assimilation methods that combine prediction and observations.
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On nonlinear least-squares filtering
Automatica, 1967This paper considers the problem of sequential estimation of the state of a non-linear process from noisy measurement data. In a previous paper [1] the problem was concerned with measurements which are linear combinations of the state variables. In this paper a different approach to the problem is used which results in filter equations for processes ...
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