中文摘要：

主要研究离散时间大规模动态系统的分布式状态估计问题。首先，将系统划分为若干个子系统，基于区域内部量测信息和邻居传递的信息，各子系统利用该算法对本地状态进行估计，降低状态变量的维数、算法的计算复杂度和通信压力。该算法独立运行，并且平行运行该算法可以有效减少整体运行时间。通过减弱约束条件，利用数学归纳法证明由该算法得到的估计误差协方差和预测误差协方差矩阵正定。根据系统能观测性秩判据和不等式技巧，证明误差协方差矩阵有上界，并且上界是有界的，保证该算法在应用中的可行性。最后通过仿真研究，验证主要结论。

英文摘要：

The problem of distributed state estimation over discrete-time large-scale dynamic systems was studied. The system was divided into some subsystem, and based on the local measurement and the information received from its neighbors, each subsystem utilized the proposed algorithm to estimate its local state, which reduced the dimension of the state vector, and enjoyed low computational complexity and communication load. This algorithm was run independently and in parallel to effectively reduce the overall execution time. By weakening the constraint condition, the mathematical induction was used to prove that the state estimation and prediction error covariance matrices obtained from this algorithm were positive definite. The rank criterion of system observability together with the inequality technique were utilized to prove that error covariance matrices had upper bounds and the upper bounds were also existence and bounded, which supported the feasibility of this algorithm in applications. At last, simulations of an example were provided to demonstrate the main results.