By combining strong tracking filter theory with state fusion estimation algorithm, we put forward a new algorithm of state fusion estimation for a class of nonlinear dynamic systems with all sensors having different sampling rates on the basis of distributed information. The algorithm is also extended to the joint state and parameter estimation of a class of nonlinear systems having time-varying parameters with unknown changing law. The effectiveness of the proposed algorithm is illustrated by computer simulations, which show that the new algorithm has strong robustness against model-plant mismatches.