Joint state and parameter estimation for multisensor nonlinear dynamic systems on the basis of strong tracking filter

By combining the strong tracking filtering theory with data fusionn estimation technology, a new joint state and parameter estimation algorithm of multisensor based on strong tracking filter is proposed. For the multisensor and single model nonlinear dynamic systems having the same sample rates for every sensor, the fusion estimate on the basis of global information by use of strong tracking filter is established, and the effectiveness of the new algorithm is also illustrated by use of an example. These give a primary solution to the fusion estimation problem having bigger errors produced by Kalman filler because of uncertainties of modeling system. This work enriches and develops the information fusion theory.