Efficient Implementation of Continuous-Discrete Extended Kalman Filters for State and Parameter Estimation of Nonlinear Dynamic Systems

Real-Time Estimation of PEMFC Parameters Using a Continuous-Discrete Extended Kalman Filter Derived from a Pseudo Two-Dimensional Model

“…formulae, which are based on the implicit midpoint method, are rearranged as follows: where I is the identity matrix of size n. Due to the application of the simplification method, it is expected that the model accuracy will be decreased, albeit the model-solving is faster [43].…”

Finite‐time mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passive filtering for nonlinear singular systems under fading channels

“…However, since Kalman filter requires that the external noise be white noise, it cannot be applied to some practical situations. 22,23 With the deepening of research, the utilization of the  ∞ filter can address the limitations of Kalman filter. Unlike Kalman filter, the  ∞ filter does not rely on assumptions about the interfering signal's characteristics.…”

“…Since then, Kalman filter has become a classic method for processing signal estimation. However, since Kalman filter requires that the external noise be white noise, it cannot be applied to some practical situations 22,23 . With the deepening of research, the utilization of the $$$ {\mathscr{H}}_{\infty } $$$ filter can address the limitations of Kalman filter.…”

IMU Networks for Trajectory Reconstruction in Logistics Applications

“…This paper adopts the formulation from [ 7 ], developed in the context of a multi-target tracking problem, with the targets and measurements assumed to have a one-to-one relation. The notation follows the usual form of the continuous-discrete (CD) extended Kalman framework (see, for instance, [ 13 ] for a critical comment and the implementation issues of this form), with the prediction stage for the i th IMU node given by the following (see also [ 14 ] for a face-off between five different formulations): where stands for the state variables, the observations, the covariance matrix, the observation noise covariance, F the Jacobian of f , and the modified update stage is as follows: where H is the Jacobian of h , is the process noise covariance matrix, and the networking and consensus are expressed through the measurement uncertainties: using the measurements, (received by the i th IMU from the j th IMU, i.e., the i th IMU is connected to , which are the other IMUs), and with the weighted innovations, is as follows: where is the weight expressing the relevance of the measurement from node j to the estimate of the measurement by node ...…”

“…This paper adopts the formulation from [7], developed in the context of a multi-target tracking problem, with the targets and measurements assumed to have a one-to-one relation. The notation follows the usual form of the continuous-discrete (CD) extended Kalman framework (see, for instance, [13] for a critical comment and the implementation issues of this form), with the prediction stage for the ith IMU node given by the following (see also [14] for a face-off between five different formulations):…”