Event Triggered Robust Cubature Kalman Filter Using Stochastic Innovational Condition

This chapter designs the stochastic event triggered robust cubature Kalman filter (SETRCKF) in order to deal with the non-Gaussian or unknown noises. Firstly, to overcome the deficiency of ETCKF in this chapter, the stochastic event triggered cubature Kalman filter (SETCKF) is developed based on the stochastic innovation based event triggered sampling strategy, which can maintain the Gaussian property of the conditional distribution of the system state. Based on SETCKF, the SETRCKF is further designed by using the moving-window estimation method and the adaptive method to estimate the measurement noise covariance matrices and the process noise covariance matrices. The Huber function is used to make SETCKF more robust. Moreover, the stochastic stabilities of these two proposed filters are analyzed by deriving the sufficient conditions regarding the stochastic stability of the filtering error. The feasibility and performance of the developed filterings are verified based on the IEEE 39 bus system.