Vehicle motion trajectory prediction is the basis of vehicle collision early warning or conflict resolution. In order to improve accuracy prediction, a method based on “current” statistical (CS) model and cubature Kalman filter (CKF) proposed. This considers acceleration variation rules in actual process state equation, so that estimated value can be consistent with real range. condition overco...

[1] Many studies have shown the deficiencies of the extended Kalman filter (EKF), even though it has become a standard technique used in nonlinear estimation. In the EKF method, the state distribution is propagated analytically through the first-order linearization of the nonlinear system, which can introduce large errors in variable estimation and may lead to suboptimal performance and sometim...

Abstract: This paper presents an application of nonlinear filtering techniques for the tracking control design of tracked mobile robot under slip condition. The slip is represented only by the longitudinal wheels slip that is described by just an unknown parameter. The extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF) are used to estimate the states of...

In this paper, the new nonlinear filter method Cubature Kalman Filter (CKF) is improved to solve the passive location problem. Firstly, the Empirical Mode Decomposition (EMD) algorithm is used to estimate the new measurement noise covariance in the filter process; And then the new covariance of the noise is brought into the circle; Meanwhile, the location process is improved by the way of squar...

We introduce the Unscented von Mises–Fisher Filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the n-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, for example when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, ...

In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorit...

Joint estimation of unknown model parameters and unobserved state componentsfor stochastic, nonlinear dynamic systems is customarily pursued via the extendedKalman filter (EKF). However, in the presence of severe nonlinearities in the equa-tions governing system evolution, the EKF can become unstable and accuracy ofthe estimates gets poor. To improve the results, in this paper w...

A new filter named the maximum likelihood-based iterated divided difference filter (MLIDDF) is developed to improve the low state estimation accuracy of nonlinear state estimation due to large initial estimation errors and nonlinearity of measurement equations. The MLIDDF algorithm is derivative-free and implemented only by calculating the functional evaluations. The MLIDDF algorithm involves t...

Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubatu...

A new adaptive Unscented Kalman Filter (UKF) algorithm for actuator failure estimation is proposed. A novel filter method with the ability to adapt to the statistical characteristics of noise is presented to improve the estimation accuracy of traditional UKFs. A new algorithm (Kalman Filter (KF) ‐based adaptive UKF), with the ability to adapt to the statistical characterist...