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Discrete time nonlinear filters with informative observations are stable
 Electr. Commun. Probab
"... Abstract. The nonlinear filter associated with the discrete time signalobservation model (Xk, Yk) is known to forget its initial condition as k → ∞ regardless of the observation structure when the signal possesses sufficiently strong ergodic properties. Conversely, it stands to reason that if the ..."
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Cited by 8 (2 self)
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Abstract. The nonlinear filter associated with the discrete time signalobservation model (Xk, Yk) is known to forget its initial condition as k → ∞ regardless of the observation structure when the signal possesses sufficiently strong ergodic properties. Conversely, it stands to reason
Nonlinear Filtering of NonGaussian Noise
, 1997
"... This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive nonGaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of na ..."
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Cited by 5 (0 self)
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This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive nonGaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem
Filter
, 2010
"... complex formulation is sought. In the quest for such a formulation, we consider regularizations of the Preserving the (skew)symmetries of the continuous differential operators when discretizing them has been shown to be a very suitable approach for direct numerical simulation (DNS) (see [1], for i ..."
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complex formulation is sought. In the quest for such a formulation, we consider regularizations of the Preserving the (skew)symmetries of the continuous differential operators when discretizing them has been shown to be a very suitable approach for direct numerical simulation (DNS) (see [1
The Problem Of Nonlinear Filtering
, 1996
"... Stochastic filtering theory studies the problem of estimating an unobservable `signal' process X given the information obtained by observing an associated process Y (a `noisy' observation) within a certain time window [0; t]. It is possible to explicitly describe the distribution of X give ..."
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Cited by 2 (1 self)
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Stochastic filtering theory studies the problem of estimating an unobservable `signal' process X given the information obtained by observing an associated process Y (a `noisy' observation) within a certain time window [0; t]. It is possible to explicitly describe the distribution of X
Predictive Filtering for Nonlinear Systems
 Journal of Guidance, Control, and Dynamics
, 1997
"... In this paper, a realtime predictive filter is derived for nonlinear systems. The major advantage of this new filter over conventional filters is that it provides a method of determining optimal state estimates in the presence of significant error in the assumed (nominal) model. The new realtime n ..."
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Cited by 9 (6 self)
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nonlinear filter determines ("predicts") the optimal model error trajectory so that the measurementminusestimate covariance statistically matches the known measurementminus truth covariance. The optimal model error is found by using a onetime step ahead control approach. Also, since
Particle and Cell Approximations for Nonlinear Filtering
, 1995
"... : We consider the nonlinear filtering problem for systems with noisefree state equation. First, we study a particle approximation of the a posteriori probability distribution, and we give an estimate of the approximation error. Then we show, and we illustrate with numerical examples, that this app ..."
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Cited by 2 (0 self)
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: We consider the nonlinear filtering problem for systems with noisefree state equation. First, we study a particle approximation of the a posteriori probability distribution, and we give an estimate of the approximation error. Then we show, and we illustrate with numerical examples
Numerical Approximations to Optimal Nonlinear Filters
, 2008
"... Two types of numerical algorithms for nonlinear filters are considered. The first is based on the Markov chain approximation method, a powerful approach to numerical problems in stochastic control. It yields an approximation to the weaksense conditional density and converges in the weak sense as th ..."
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Cited by 3 (0 self)
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Two types of numerical algorithms for nonlinear filters are considered. The first is based on the Markov chain approximation method, a powerful approach to numerical problems in stochastic control. It yields an approximation to the weaksense conditional density and converges in the weak sense
Filters
"... DESIGN SHOWCASE Lowcost stepup/stepdown converter accepts 2V to 16V inputs 10 Visiblelaser driver has digitally controlled power and modulation 11 Highvoltage circuit breaker protects to 26V 13 Dual comparator forms temperaturecompensated proximity detector 15 ..."
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DESIGN SHOWCASE Lowcost stepup/stepdown converter accepts 2V to 16V inputs 10 Visiblelaser driver has digitally controlled power and modulation 11 Highvoltage circuit breaker protects to 26V 13 Dual comparator forms temperaturecompensated proximity detector 15
Learning from demonstration
 Advances in Neural Information Processing Systems 9
, 1997
"... By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstra ..."
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Cited by 392 (32 self)
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By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and
Numerical Methods for Nonlinear Filtering
"... This note briefly summarizes numerical approaches to state estimation (filtering) for nonlinear discrete time continuous state Markov systems. . . 1 Notation Conditional probability densities are denoted dp(xj : : :). These are densities in x  measures rather than numerically valued functions. ..."
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This note briefly summarizes numerical approaches to state estimation (filtering) for nonlinear discrete time continuous state Markov systems. . . 1 Notation Conditional probability densities are denoted dp(xj : : :). These are densities in x  measures rather than numerically valued functions
Results 1  10
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468,939