Distributed Kalman estimation with decoupled local filters

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...

Parameter Estimation Using Kalman Filters with Constraints

We suggest incorporating dynamical information such as locations of unstable fixed points into parameter estimation algorithms in order to improve the method of reconstructing dynamics from time series data. We show how the process of reconstruction using nonlinear filters such as the extended Kalman filter can be easily modified to take advantage of the additional information. We demonstrate t...

Decoupled Extended Kalman Filters Improve Long-Term Online Prediction

Joint Channel Estimation and Data Detection with Cubature Kalman Filters

Resumo— Este artigo apresenta a utilização do cubature Kalman filter para a estimação dos coeficientes de um canal MIMO duplamente seletivo e detecção de símbolos discretos de forma conjunta. Com base no modelamento da dinâmica temporal dos coeficientes do canal e dos símbolos transmitidos, um vetor de estados é definido contendo ambas informações levando a uma não-lineraridade na equação de ob...

Orientation Estimation Using Kalman Filters with Multiple Sensors

The ability to repeatedly and accurately position robotic end-effectors is a key requirement for modern industrial automation. Parallel developments in domestic and service robotics are opening up exciting possibilities for future manufacturing scenarios that would see robots and humans work far more closely together. These robots will typically be lighter, more mobile, less task specific and f...