From Intrinsic Optimization to Iterated Extended Kalman Filtering on Lie Groups

Extended Kalman Filtering by

Adaptive Chaos Synchronization Based on the Iterated Extended Kalman Filtering Trained Radial Basis Function Neural Networks

Based on the iterated extended Kalman filtering trained radial basis function neural networks, this contribution investigates the adaptive synchronization problem between different chaotic systems in presence of unknown system parameters, with random noises to the observed outputs and without needing to estimate the system parameters. The input vector to the radial basis function neural network...

Iterated extended Kalman smoothing with expectation-propagation

We formulate extended Kalman smoothing in an expec­ tation-propagation (EP) framework. The approximation involved (a local linearization) can be looked upon as a 'collapse' of a non­ gaussian belief state onto a Gaussian form. This formulation al­ lows us to come up with better approximations to the belief states, since we can iterate the algorithm until no further refinement of the beliefs is ...

Synchronization through Extended Kalman Filtering

We study the synchronization problem in discrete-time via an extended Kalman filter (EKF). That is, synchronization is obtained of transmitter and receiver dynamics in case the receiver is given via an extended Kalman filter that is driven by a noisy drive signal from the transmitter. Extensive computer simulations show that the filter is indeed suitable for synchronization of (noisy) chaotic t...

Monte Carlo Filtering on Lie Groups

We propose a nonlinear lter for estimating the trajectory of a random walk on a matrix Lie group with constant computational complexity. It is based on a nite-dimensional approximation of the conditional distribution of the state given past measurements via a set of fair samples, which are updated at each step and proven to be consistent with the updated conditional distribution. The algorithm ...