This article addresses the distributed state estimation problem for uncertain time‐varying dynamic systems with state constraints over a sensor network. By using a null space method, the distributed state estimation problem for uncertain dynamic systems with state constraints can be cast into a new unconstrained distributed state estimation problem for reduced uncertain dynamic systems. A constrained distributed Kalman filter is proposed, and it is shown that the full state estimates can be recovered at any time and satisfy the constraints. An optimized upper bound of the estimation error covariance of each sensor is obtained, and the corresponding gains are designed. The application conditions of the proposed algorithm are mild, and they can be off‐line checked. Furthermore, the computational requirements in this article are also reduced compared with the existing results. Finally, the performance of the proposed filter algorithm is demonstrated through numerical simulations.

中文翻译：

---

具有状态约束的不确定动态系统的分布式卡尔曼滤波

本文解决了传感器网络上具有状态约束的不确定时变动态系统的分布式状态估计问题。通过使用零空间方法，可以将具有状态约束的不确定动态系统的分布状态估计问题转化为用于减少不确定性动态系统的新的无约束分布状态估计问题。提出了一种约束分布式卡尔曼滤波器，证明了全状态估计可以在任何时候恢复并且满足约束条件。获得了每个传感器的估计误差协方差的优化上限，并设计了相应的增益。该算法的应用条件比较温和，可以离线检查。此外，与现有结果相比，本文的计算要求也有所降低。最后，通过数值仿真证明了所提出的滤波算法的性能。