Adaptive Extended Kalman Filter for Nonlinear System with Noise Compensating Technology

Abstract

This paper is concerned with the development of new adaptive nonlinear Kalman estimators which incorporate nonlinear model errors and noise statistical characteristic errors. With the adoption of fictitious noise compensation technique and actual non-divergent computation method, the new filters are aimed at compensating the nonlinear dynamics as well as the system modeling errors by adaptively estimating the noise statistics and unknown parameters. The performance of the proposed adaptive estimators is demonstrated using six-state with varying model parameters as a simulation example.