Cubature H-∞ Information Filter and its Extension

State estimation for nonlinear systems with Gaussian or non-Gaussian noises, and with single and multiple sensors, is presented. The key purpose is to propose a derivative free estimator using concepts from the information filter, the H∞H∞ filter, and the cubature Kalman filter (CKF). The proposed estimator is called the cubature H∞H∞ information filter (CH∞IFCH∞IF); it has the capability to deal with highly nonlinear systems like the CKF, like the H∞H∞ filter it can estimate states with stochastic or deterministic noises, and similar to the information filter it can be easily extended to handle measurements from multiple sensors. A numerically stable square-root CH∞IFCH∞IF is developed and extended to multiple sensors. The CH∞IFCH∞IF is implemented to estimate the states of a nonlinear permanent magnet synchronous motor model. Comparisons are made with an extended H∞