This brief technical note elaborates three well-known state estimators, which are used extensively in practice. These are the rather old-fashioned extended Kalman filter (EKF) and the recently-designed cubature Kalman filtering (CKF) and un-scented Kalman filtering (UKF) algorithms. Nowadays, it is commonly accepted that the contemporary techniques outperform always the traditional EKF in the a...

The importance of a system theory based approach in understanding immunological diseases, in particular the HIV-1 infection, is being increasingly recognized. This is because the dynamics of virus infection may be effectively represented by relatively compact state space models in the form of nonlinear ordinary differential equations. This work focuses on the identification of constant and time...

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...

In this paper the techniques of extended Kalman filtering (EKF) and hidden Markov mode! (HMM) signal processing are combined to adaptively demodulate quadrature amplitude-modulated (QAM) signals in noisy fading channels. This HMM approach is particularly suited to signals for which the message symbols are not equally probable, as is the case with many types of coded signals. Our approach is to ...

Back-Propagation (BP), Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) are three of the most widely used algorithms for training feed forward Neural Networks (NNs). This paper presents an accurate DGPS land vehicle navigation system using multi-layered NNs based on the BP, EKF and PSO learning algorithms. The network setup is developed based on mathematical models to avoid ex...

This paper presents the development of two distributed terrestrial radiolocation algorithms that use range estimates derived from DS-CDMA waveforms. The first algorithm, which is RLS-based, is derived as the solution of an approximate least-squares positioning problem. This algorithm has the advantage of reduced computational complexity, compared with the EKF-based algorithm that is presented. ...

The extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation in vision tracking. In this work, we present an alternative filter with performance superior to that of the EKF. This algorithm, referred to as the Particle filter. Particle filtering was originally developed to track objects in clutter (multi-modal distribution). We present as...

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demon...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contain...