The goal of this paper is to give a survey of the previous works on the globally optimal distributed Kalman filtering fusion with classical and nonclassical dynamic systems. Then, we summarize some of our recent results on nonclassical and unideal dynamic systems, including dynamic systems with feedback and cross-correlated sensor measurement noises, dynamic systems with random parameter matrices, and dynamic systems with out-of-sequence or asynchronous measurements. The global optimality in this paper means that the distributed Kalman filtering fusion is exactly equal to the corresponding centralized optimal Kalman filtering fusion. Therefore, not only all of the proposed fusion algorithms here are distributed, but performance as good as that of the corresponding optimal centralized fusion algorithms is achieved. There also exist many papers for other fusion optimality (e.g., the optimal convex linear estimation/compression fusion) discussion, which are not involved in this paper. The goal of this paper is to give a survey of the previous works on the globally optimal distributed Kalman filtering fusion with classical and nonclassical dynamic systems. Then, we summarize some of our recent results on nonclassical and unideal dynamic systems, including dynamic systems with feedback and cross-correlated sensor measurement noises, dynamic systems with random parameter matrices, and dynamic systems with out-of-sequence or asynchronous measurements. The global optimality in this paper means that the distributed Kalman filtering fusion is exactly equal to the corresponding centralized optimal Kalman filtering fusion. Therefore, not only all of the proposed fusion algorithms here are distributed, but performance as good as that of the corresponding optimal centralized fusion algorithms is achieved. There are many papers for other fusion optimality (eg, the optimal convex linear estimation / compression fusion) discussion, which are not involved in this paper.