Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	甘叠，中关村实验室
Inviter:
Title:	Stability analysis of distributed Kalman filtering algorithm for stochastic regression model
Language:	Chinese
Time & Venue:	2024.01.21 10:20-11:00 思源楼425
Abstract:	The work proposes a distributed Kalman filtering (KF) algorithm to track a time-varying unknown signal process for a stochastic regression model based on a diffusion strategy over sensor networks in a cooperative way. Unlike the centralized algorithms, no fusion center is needed here, which means that the distributed KF algorithm is more robust and scalable. Moreover, we provide the theoretical analysis on the stability of the proposed distributed KF algorithm without independent and stationary signal assumptions, which implies that the theoretical results are expected to be applied to stochastic feedback systems. Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and nonstationary random matrices involved in the error equation. We employ analysis techniques such as stochastic Lyapunov function, stability theory of stochastic systems, and algebraic graph theory to deal with the above issue. The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal, the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.