ABSTRACT State estimation procedures using the extended Kalman filter are investigated for a transient heat transfer problem in which a heat source is applied on one side of a thin plate and ultrasonic time of flight or temperature is measured on the other side of the plate. A forward conduction solution is developed using finite element methods and verified using closed-form analytic solutions...

Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for linear systems and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the exte...

The principle of the iterated extended Kalman filter has been generalized to create a new filter that has superior performance when the estimation problem contains severe nonlinearities. The new filter is useful when nonlinearities might significantly degrade the accuracy or convergence reliability of other filters. The new filter solves a nonlinear smoothing problem for the current and past sa...

State estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. ...

The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the...

In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterat...

A neural extended Kalman filter algorithm was embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which, uses a high process noise ...

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustness...