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...

Due to the six unreasonable assumptions companioned with the Black-Scholes options pricing model (BSM), which often make the miss-pricing result because of the difference of market convention in practical. This study try to combine the BSM and extended Kalman filters-based artificial neural networks (EKF-ANN) to deal with the limitation of consideration of the influences from many unexpected re...

The Kalman lter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be diicult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearises all nonlinear models so that the traditional linear Kalman lter can be applied. Al...

A new filter is proposed that achieves reliable state estimation in nonlinear systems with multiple equilibrium points. The latter may exhibit strong nonlinearity and sudden transient behavior triggered by either system process noise, or observation noise, or outliers. Our filter is able to output the correct qualitative state over time quickly and reliably when the system dynamics experience s...

The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearises all nonlinear models so that the traditional linear Kalman filter can be appli...

Chemical vapor deposition (CVD) is an important step in integrated circuit fabrication and is a dynamic process that is complex and subject to unmeasured disturbances. Currently in semiconductor industry the main form of advanced process control for CVD is run-to-run (R2R) control. R2R control is a form of discrete process control in which the processing recipe is modified ex situ based on post...

To assist nature observation, we take on the challenge of search for rare bird species using a single fixed camera. To reduce the huge amount of data for identification, we develop a model-based filtering approach that verifies the bird body axis information with the known bird flying dynamics. As a commonly used method, an extended Kalman filter (EKF) cannot be directly applied because the EKF...

Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) [6]. The EKF combines the sensor measurements with predictions coming from a model of vehicle motion (either dynamic or kinemati...

The ensemble Kalman filter (EnKF) is a technique for dynamic state estimation. EnKF approximates the standard extended Kalman filter (EKF) by creating an ensemble of model states whose mean and empirical covariance are then used within the EKF formulas. The technique has a number of advantages for large-scale, nonlinear problems. First, large-scale covariance matrices required within EKF are re...