Robust Extended Kalman Filtering for Nonlinear Singular System with Uncertain Variance Colored Noises

In this paper, for the stochastic singular systems with uncertain variance colored noises and nonlinear measurement, a robust extended Kalman filtering algorithm is proposed. The original singular system is transformed into a standard system by using the singular value decomposition (SVD) method and the difference transformation method. The Taylor series expansion method is applied to handle nonlinear measurement, and the higher-order terms of the linearization error are represented by norm-bounded uncertain matrices. Further, a robust filter is presented and the variance constrained method is adopted to acquire the filtering gain that minimizes the upper bound of the filtering error variance. Finally, one simulation example is exhibited to demonstrate the effectiveness of the proposed algorithm.