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Gaussian Filter for Nonlinear Filtering Problems
, 2000
"... In this paper we develop and analyze realtime and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We presentthesystematic formulation of Gaussian filters and develop efficientand accurate numerical integration of the proposed filter. We also discuss the mixed ..."
Abstract

Cited by 156 (0 self)
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In this paper we develop and analyze realtime and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We presentthesystematic formulation of Gaussian filters and develop efficientand accurate numerical integration of the proposed filter. We also discuss the mixed
Unscented Filtering and Nonlinear Estimation
 PROCEEDINGS OF THE IEEE
, 2004
"... The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the ..."
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Cited by 566 (5 self)
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The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear
Idempotent nonlinear filters
 Proc. Nonlinear Signal Image Processing Conf. (NSIP’03), GradoTrieste
, 2003
"... Idempotent nonlinear filters may be viewed as extensions of the class of (nonrealizable) ideal linear filters, and one of their characteristic features is that they reduce any input sequence {xk} toarootafter one pass. This paper explores some new constructions of idempotent nonlinear filters, usin ..."
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Cited by 1 (1 self)
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Idempotent nonlinear filters may be viewed as extensions of the class of (nonrealizable) ideal linear filters, and one of their characteristic features is that they reduce any input sequence {xk} toarootafter one pass. This paper explores some new constructions of idempotent nonlinear filters
Optimum Nonlinear Filtering
 IEEE Transactions on Signal Processing
, 1997
"... This paper is composed of two parts. The first part surveys the literature regarding optimum nonlinear filtering from the (continuoustime) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discretetime context) to nonlinear fi ..."
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Cited by 7 (0 self)
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This paper is composed of two parts. The first part surveys the literature regarding optimum nonlinear filtering from the (continuoustime) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discretetime context) to nonlinear
STABILITY OF NONLINEAR FILTERS IN
"... The nonlinear filtering equation is said to be stable if it “forgets ” the initial condition. It is known that the filter might be unstable even if the signal is an ergodic Markov chain. In general, the filtering stability requires stronger signal ergodicity provided by the, so called, mixing condit ..."
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The nonlinear filtering equation is said to be stable if it “forgets ” the initial condition. It is known that the filter might be unstable even if the signal is an ergodic Markov chain. In general, the filtering stability requires stronger signal ergodicity provided by the, so called, mixing
Nonlinear Filtering of Electron
 in Advances in Neural Information Processing Systems 16
, 2003
"... Nonlinear filtering can solve very complex problems, but typically involve very time consuming calculations. Here we show that for filters that are constructed as a RBF network with Gaussian basis functions, a decomposition into linear filters exists, which can be computed e#ciently in the frequ ..."
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Nonlinear filtering can solve very complex problems, but typically involve very time consuming calculations. Here we show that for filters that are constructed as a RBF network with Gaussian basis functions, a decomposition into linear filters exists, which can be computed e
NONLINEAR FILTERING AND LARGE DEVIATIONS *
, 1887
"... We consider the nonlinear filtering problem dz = f(z)dt + fidw, dy = h(z)dt + fidu, and obtain linqoElogq'(z,t) =W (z, t) for unnormalised conditional densities q'(z, t) using PDE methods. Here, W(z,t) is the value function for a deterministic optimal control problem arising in Mortensen ..."
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We consider the nonlinear filtering problem dz = f(z)dt + fidw, dy = h(z)dt + fidu, and obtain linqoElogq'(z,t) =W (z, t) for unnormalised conditional densities q'(z, t) using PDE methods. Here, W(z,t) is the value function for a deterministic optimal control problem arising
Exponential Stability for Nonlinear Filtering
, 1996
"... We study the a.s. exponential stability of the optimal filter w.r.t. its initial conditions. A bound is provided on the exponential rate (equivalently, on the memory length of the filter) for a general setting both in discrete and in continuous time, in terms of Birkhoff's contraction coefficie ..."
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Cited by 59 (2 self)
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We study the a.s. exponential stability of the optimal filter w.r.t. its initial conditions. A bound is provided on the exponential rate (equivalently, on the memory length of the filter) for a general setting both in discrete and in continuous time, in terms of Birkhoff's contraction
A New Extension of the Kalman Filter to Nonlinear Systems
, 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
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Cited by 778 (6 self)
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The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF
Nonlinear Filtering by Threshold Decomposition
, 1999
"... A new threshold decomposition architecture is introduced to implement stack filters. The architecture is also generalized to a new class of nonlinear filters known as threshold decomposition (TD) filters which are shown to be equivalent to the class of Llfilters under certain conditions. Another ne ..."
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Cited by 1 (0 self)
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A new threshold decomposition architecture is introduced to implement stack filters. The architecture is also generalized to a new class of nonlinear filters known as threshold decomposition (TD) filters which are shown to be equivalent to the class of Llfilters under certain conditions. Another
Results 1  10
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