State and information space estimation: a comparison

State and information space estimation methods used in both linear and nonlinear systems are compared. The (linear) information filter is introduced as an algebraic equivalent to the Kalman filter. Linear information space is extended to nonlinear information space by outlining the extended information filter. The algebraic equivalence of this filter to the extended Kalman filter and the benefits of nonlinear information space are illustrated by considering a system involving both nonlinear state evolution and nonlinear observations.