Universal linearization concept for extended Kalman filters

Systematic Estimation of State Noise Statistics for Extended Kalman Filters

The successful application of model-based control depends on the information about the states of the dynamic system. State-estimation methods, like extended Kalman filters ( ) EKF , are useful for obtaining reliable estimates of the states from a limited number of measurements. They also can handle the model uncertainties and the effect of unmeasured disturbances. The main issue in applying EKF...

Nonlinear state observers and extended Kalman filters for battery systems

The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance m...

Decoupled Extended Kalman Filters Improve Long-Term Online Prediction

Frequency Line Tracking , Extended Kalman Filters and Some HMM Problems

ire review the use of Kalman filter and HMM techniques for frequency tracking (single-tone and multiharmonic cases). A possible new HMM structure is suggested and issues related to rhresholding phenomena are discussed.

Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters

Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform traditional RNNs when dealing with sequences involving not only short-term but also long-term dependencies. The decoupled extended Kalman filter learning algorithm (DEKF) works well in online environments and reduces significantly the number of training steps when compared to the standard gradient-descent algorithms. Prev...