Information based estimation for both linear and nonlinear systems

A new estimation algorithm is derived and appraised for nonlinear systems. The notion and measures of information are defined and this leads to a discussion of the algebraic equivalent of the Kalman filter, the linear information filter. Examples of dynamic systems are simulated to illustrate the algebraic equivalence of the Kalman and information filters. The benefits of information space are also explored. Estimation for systems with nonlinearities is then considered starting with the extended Kalman filter. Linear information space is extended to nonlinear information space by deriving the extended information filter. The advantages of the extended information filter over the extended Kalman filter are demonstrated for systems involving both nonlinear state evolution and nonlinear observations