Covariance Intersection Fusion Estimator for Nonlinear System with correlated noise
Ming Zhao · 2019
In this paper, for the nonlinear system with correlated noise, the decorrelation method is applied to obtain the noise independent nonlinear system. Furthermore, the Unscented Kalman Filter method is applied to obtain the nonlinear estimator. In order to improve the filtering accuracy, this paper uses the covariance intersection fusion algorithm. The algorithm avoids solving the covariance matrix, and the algorithm is simple and easy to implement. A simulation example verifies the correctness and effectiveness of the algorithm.