TY  - CONF
N1  - cited By 3; Conference of 4th International Conference on Fundamental and Applied Sciences, ICFAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125141
N2  - Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points. © 2016 Author(s).
TI  - Nonlinear dynamical system identification using unscented Kalman filter
ID  - scholars6634
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005982804&doi=10.1063%2f1.4968052&partnerID=40&md5=1ec8ef8fd2abdc8a50f1131227abb3c5
A1  - Rehman, M.J.U.
A1  - Dass, S.C.
A1  - Asirvadam, V.S.
VL  - 1787
Y1  - 2016///
SN  - 0094243X
PB  - American Institute of Physics Inc.
ER  -