Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation

Face Recognition and Tracking in Videos

“…Elafi et al [34] proposed an approach that can detect and track multiple moving objects automatically without any learning phase or prior knowledge about the size, nature or initial position. Gyorgy et al [35] proposed Extended Kalman filter for non linear systems. Kumar et al [36] extracted moving objects using background subtraction and applied Kalman filter for successive tracking.…”

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

“…Using the state-update function | −1 = ( −1 , −1 ) to transform the sigma points and then calculate the a priori state estimate using (3) and covariance using (4). The function (⋅,⋅) is related to the model of the vehicle's dynamic (Gyorgy et al, 2014).…”

“…4) Update the output vectors. Transform the sigma points through the measurement-update function ℎ(⋅) (Gyorgy et al, 2014), and calculate the mean and covariance of the measurement vector according to (5) and (6) respectively.…”

A Review on Tuning of Extended Kalman Filter using Optimization Techniques for State Estimation

“…To evaluate the efficiencies of the proposed algorithm 22 benchmark are used which are divided in three categories: Unimodal; Multimodal and Composite [19]. In [20], the Unscented Kalman Filter and Particle Filter are compared with Extended Kalman Filter to estimate the state for various non-linear systems. Unscented Kalman Filter uses the deterministic sampling approach and Particle Filter is based on statistical signal processing.…”