Distributed State Estimation Over Sensor Networks With Substate Decomposition Approach

This paper investigates the issue of distributed state estimation for discrete-time systems over sensor networks. To reduce the computational complexity of each sensor, the system state is decomposed by the substate decomposition approach based on the measurements. A distributed estimator is designed according to the decomposed dynamic systems. In the meantime, a diffusion strategy with different steps is introduced to improve the performance of the distributed estimator. An upper bound of the prediction error covariance is derived via Young's inequality, and it is minimized by designing a suboptimal estimator gain. A sufficient condition is obtained to guarantee the boundedness of the upper bound based on the detectability of each source component. Finally, the effectiveness of the proposed distributed estimation algorithm is validated via simulation.

Automated summary

This paper investigates the issue of distributed state estimation for discrete-time systems over sensor networks. A distributed estimator is designed according to the decomposed dynamic systems and a diffusion strategy with different steps is introduced to improve the performance. The upper bound of the prediction error covariance is derived and minimized by a suboptimal estimator gain. The effectiveness of the proposed algorithm is validated through simulation.