This paper proposes an estimation technique which employs measures of information for nonlinear systems. General recursive estimation and in particular the Kalman filter is discussed. A Bayesian approach to probabilistic information fusion is outlined. The notion and measures of information are defined. This leads to the derivation of the algebraic equivalent of the Kalman filter, the linear information filter. The characteristics of this filter and the advantages of information space estimation are discussed. State estimation for systems with nonlinearities is considered and the extended Kalman filter treated. Linear information space is then extended to nonlinear information space by deriving the extended information filter. This establishes all the necessary mathematical tools required for exhaustive information space estimation. The advantages of the extended information filter over the extended Kalman filter are presented and demonstrated. This extended information filter constitutes an original and significant contribution to estimation theory made in this paper. It forms the basis of the decentralized data fusion techniques which can be applied to a modular wheeled mobile robot.