The Experts below are selected from a list of 35115 Experts worldwide ranked by ideXlab platform
Danilo P Mandic - One of the best experts on this subject based on the ideXlab platform.
-
an augmented Extended Kalman Filter algorithm for complex valued recurrent neural networks
Neural Computation, 2007Co-Authors: Su Lee Goh, Danilo P MandicAbstract:An augmented complex-valued Extended Kalman Filter (ACEKF) algorithm for the class of nonlinear adaptive Filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.
-
an augmented Extended Kalman Filter algorithm for complex valued recurrent neural networks
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: Su Lee Goh, Danilo P MandicAbstract:An augmented complex-valued Extended Kalman Filter (ACEKF) algorithm for the class of nonlinear adaptive Filters realised as fully connected recurrent neural networks (FCRNNs) is introduced. The algorithm is derived based on the recent developments in augmented complex statistics, and the Jacobian matrix within the ACEKF algorithm is computed using a general fully complex real time recurrent learning (CRTRL) algorithm. This makes ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach
Rolf Unbehauen - One of the best experts on this subject based on the ideXlab platform.
-
stochastic stability of the discrete time Extended Kalman Filter
IEEE Transactions on Automatic Control, 1999Co-Authors: Konrad Reif, Stefan Gunther, Edwin E Yaz, Rolf UnbehauenAbstract:The authors analyze the error behavior for the discrete-time Extended Kalman Filter for general nonlinear systems in a stochastic framework. In particular, it is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough. This result is verified by numerical simulations for an example system.
-
The Extended Kalman Filter as an exponential observer for nonlinear systems
IEEE Transactions on Signal Processing, 1999Co-Authors: Konrad Reif, Rolf UnbehauenAbstract:We analyze the behavior of the Extended Kalman Filter as a state estimator for nonlinear deterministic systems. Using the direct method of Lyapunov, we prove that under certain conditions, the Extended Kalman Filter is an exponential observer, i.e., the dynamics of the estimation error is exponentially stable. Furthermore, we discuss a generalization of the Kalman Filter with exponential data weighting to nonlinear systems
Konrad Reif - One of the best experts on this subject based on the ideXlab platform.
-
stochastic stability of the Extended Kalman Filter with intermittent observations
IEEE Transactions on Automatic Control, 2010Co-Authors: Sebastian Kluge, Konrad Reif, Martin BrokateAbstract:In this technical note, we analyze the error behavior of the discrete-time Extended Kalman Filter for nonlinear systems with intermittent observations. Modelling the arrival of the observations as a random process, we show that, given a certain regularity of the system, the estimation error remains bounded if the noise covariance and the initial estimation error are small enough. We also study the effect of different measurement models on the bounds for the error covariance matrices.
-
stochastic stability of the discrete time Extended Kalman Filter
IEEE Transactions on Automatic Control, 1999Co-Authors: Konrad Reif, Stefan Gunther, Edwin E Yaz, Rolf UnbehauenAbstract:The authors analyze the error behavior for the discrete-time Extended Kalman Filter for general nonlinear systems in a stochastic framework. In particular, it is shown that the estimation error remains bounded if the system satisfies the nonlinear observability rank condition and the initial estimation error as well as the disturbing noise terms are small enough. This result is verified by numerical simulations for an example system.
-
The Extended Kalman Filter as an exponential observer for nonlinear systems
IEEE Transactions on Signal Processing, 1999Co-Authors: Konrad Reif, Rolf UnbehauenAbstract:We analyze the behavior of the Extended Kalman Filter as a state estimator for nonlinear deterministic systems. Using the direct method of Lyapunov, we prove that under certain conditions, the Extended Kalman Filter is an exponential observer, i.e., the dynamics of the estimation error is exponentially stable. Furthermore, we discuss a generalization of the Kalman Filter with exponential data weighting to nonlinear systems
Su Lee Goh - One of the best experts on this subject based on the ideXlab platform.
-
an augmented Extended Kalman Filter algorithm for complex valued recurrent neural networks
Neural Computation, 2007Co-Authors: Su Lee Goh, Danilo P MandicAbstract:An augmented complex-valued Extended Kalman Filter (ACEKF) algorithm for the class of nonlinear adaptive Filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.
-
an augmented Extended Kalman Filter algorithm for complex valued recurrent neural networks
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: Su Lee Goh, Danilo P MandicAbstract:An augmented complex-valued Extended Kalman Filter (ACEKF) algorithm for the class of nonlinear adaptive Filters realised as fully connected recurrent neural networks (FCRNNs) is introduced. The algorithm is derived based on the recent developments in augmented complex statistics, and the Jacobian matrix within the ACEKF algorithm is computed using a general fully complex real time recurrent learning (CRTRL) algorithm. This makes ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach
Sergio M Savaresi - One of the best experts on this subject based on the ideXlab platform.
-
on the parametrization and design of an Extended Kalman Filter frequency tracker
IEEE Transactions on Automatic Control, 2000Co-Authors: S Bittanti, Sergio M SavaresiAbstract:The problem of estimating the frequency of a harmonic signal embedded in broad-band noise is considered. The paper focuses on the Extended Kalman Filter frequency tracker, which is the application of the Extended Kalman Filter (EKF) framework to the frequency estimation problem. The EKF frequency tracker recently proposed in the literature is characterized by a vector of three design parameters {q,r,/spl epsi/}, whose role and tuning is still a controversial and unclear issue. In this paper it is shown that a wise parametrization of the Extended Kalman frequency tracker is characterized by just one parameter: the /spl epsi/ must be set to zero to achieve the basic property of unbiasedness in a noise-free setting; the performances of the tracker are not influenced independently by q and r; and what really matters is the ratio /spl lambda/=r/q only. The proposed simplification of the Extended Kalman Filter frequency tracker allows an easier and more transparent tuning of its tracking behavior.
