State Estimation for Nonlinear Systems Using Restricted Genetic Optimization

Abstract. In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterated Filter) and MSF (Monte-Carlo Simulation Filter) in the presence of diferents levels of noise.

• Type: Conference
• Year: 1998
• Where: IEAAIE
• Authors: Santiago Garrido, Luis Moreno, Carlos Balaguer
• Keywords: Artificial Intelligence, Extended Kalman Filter, IEAAIE 1998, Second-order Nonlinear Filter, Single-stage Iterated Filter