In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practi...

One of the most important problems when designing controller is how to deal with all kinds of uncertainties, which, along with the high nonlinearities of most real systems, makes it difficult to guarantee the desired closed loop performance. Recently, nonlinear Kalman-class filter has been extensively researched and several well-known algorithms, including Extended Kalman Filter (EKF), Unscente...

To develop an advanced battery estimation unit for electric vehicles application, the state-of-charge (SoC) estimation is proposed with an unscented Kalman filter (UKF) and realized with the RTOS lCOS-II platform. Kalman filters are broadly used to deploy various battery SoC estimators recently. Herein, an UKF algorithm has been employed to develop a systematic adaptive SoC estimation framework...

Recently, the Spherical Motion Models (SMM’s) have been introduced [1]. These new models have been developed for 3D local landmark-base Autonomous Navigation (AN). This paper is revealing new arguments and experimental results to support the SMM’s characteristics. The accuracy and the robustness in performing a specific task are the main concerns of the new investigations. To analyze their perf...

This paper presents a novel chaotic communication method using an Unscented Kalman Filter (UKF). Applying UKF, the method proposes the estimation of the state variables of the chaotic dynamical system and synchronization. The proposed method is then applied to new private secure communication. The chaotic synchronization is implemented by the UKF in the presence of processing noise and measurem...

Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the Ex...

Purpose – The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics. Design/methodology/approach – The introduced adaptive filter scheme is composed of two parallel UKFs. At every time-step, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UK...

This paper presents a performance evaluation of nonlinear filtering with Interacting Multiple Model (IMM) algorithm for implementation on Indonesian coastal radar target tracking system. On this radar, target motion is modeled using Cartesian coordinate but target position measurements are provided in polar coordinate (range and azimuth). For this implementation, we investigated two types of no...

Parameter estimation is considered to be one of the greatest challenges in computational systems biology. Biological experiments can measure only a fraction of the kinetic parameters and the rest has to be estimated in silico. Recently parameter estimation problems have been addressed in the framework of control theory. One of the most successful and widely used methods in control theory for es...

Relative navigation is a challenging technological component of many planned NASA and ESA missions. It typically uses recursive filters to fuse measurements (e.g., range and angle) from sensors with contrasting accuracies to estimate the vehicle state vectors in real time. The tendency of Extended Kalman filter to diverge under these conditions is well documented in the literature. As such, we ...