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...

Face detection and tracking are of the most challenging problems of the object tracking field because of the large variability of faces and facial expressions. In this paper, two different algorithms for face tracking based on unscented Kalman filter (UKF) are proposed. The first proposed algorithm is UKF based on Viola-Jones algorithm. Viola-Jones is extremely fast feature computation, efficie...

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...

Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. This paper describes a sensor based navigation approach using an interval analysis (IA) based adaptive mechanism for an Unscented Kalman filter (UKF). The robot is equipped with inertial sensors (INS), encoders and ultrasonic sensors. An UKF is used to estimate the robot position using ...

This paper focuses on the update step of Bayesian nonlinear ltering. We rst derive the unscented Gaussian likelihood approximation lter (UGLAF), which provides a Gaussian approximation to the likelihood by applying the unscented transformation to the inverse of the measurement function. The UGLAF approximation is accurate in the cases where the unscented Kalman lter (UKF) is not and the other w...

Recurrent neural networks (RNNs) trained with gradient-based algorithms such as real-time recurrent learning or back-propagation through time have a drawback of slow convergence rate. These algorithms also need the derivative calculation through the error back-propagation process. In this paper, a derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully c...

The continue-time unscented Kalman filter (UKF) is developed to estimate the state of a jet transport aircraft. The UKF is based on the nonlinear longitudinal aircraft equations of motion, and it is designed to provide estimates of horizontal and vertical atmospheric wind inputs. The optimal state and disturbance estimates are incorporated in feedback control laws based on the slowand fast-time...

The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state estimation with both additive and nonadditive noise structures are presented and compared. Three different Global Positioning System (GPS)/inertial navigation system (INS) sensor fusion formulations for attitude estimation are used as case studies for the nonlinear state estimation problem. A diverse set of a...

The Unscented Kalman Filter (UKF) was applied to state and parameter estimation of a one degree of freedom robot link with an elastic, cable-driven transmission. Only motor encoder and command torque data was used as input to the filter. The UKF was used offline for joint state and model-parameter estimation, and online for state estimation. This paper presents an analysis of the robustness of ...

This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because...