In this paper the Nonlinear Information Filter is derived from the Extended Kalman Filter. A nonlinear system is considered. Linearizing the state and observation equations, a linear estimator which keeps track of total state estimates is conceived; the Extended Kalman Filter. The linearized parameters and filter equations are expressed in information space. This gives a filter that predicts and estimates information about nonlinear state parameters given nonlinear observations and nonlinear system dynamics. The Nonlinear Information Filter derivation is contrasted to that of the Linear Information filter. Pitfalls of a naive extension of the later to the former are thus identified. Furthermore, the Nonlinear Information filter is decentralized and distributed, to give the Distributed and Decentralized Nonlinear Information. Application is real decentralized data fusion and distributed control is proposed. Specifically, realtime distributed/decentralized control of a navigating, modular wheeled robot is considered.
Arthur G. O. Mutambara,
Hugh F. Durrant-Whyte,
"Nonlinear information space: a practical basis for decentralization", Proc. SPIE 2355, Sensor Fusion VII, (6 October 1994); doi: 10.1117/12.189045; https://doi.org/10.1117/12.189045