报告名称：Distributed dynamic state estimation in sensor networks: Consistency, confidence, and convergence
报告时间：2019年6月25日 (星期二) 15：00—16：00
报告摘要：The problem of distributed dynamic state estimation using networked local agents with sensing and communication abilities, has become a popular research area in recent years due to its wide range of applications such as target tracking, region monitoring and area surveillance. Specifically, we consider the scenario where the local agents take local measurements and communicate with only their nearby neighbors to estimate the state of interest in a cooperative and fully distributed manner. A distributed hybrid information fusion (DHIF) algorithm is proposed in the scenario where the process model of the target and the sensing models of the local agents are linear and time varying. The proposed DHIF algorithm is shown to be fully distributed and hence scalable, to be run in an automated manner and hence adaptive to locally unknown changes in the network, to have agents communicate for only once during each sampling time interval and hence inexpensive in communication, and to be able to track the interested state with uniformly upper bounded estimate error covariance. It is also explored very mild conditions on general directed time-varying graphs and joint network observability/detectability to guarantee the stochastic stability of the proposed algorithm.
报告人概况：任伟教授，美国加州大学河滨分校（University of California, Riverside）教授。2004年在美国杨百翰大学（Brigham Young University）获得博士学位。他的主要研究方向是多智能体系统的分布式协调控制，在Springer出版了两本专著。任伟教授于2017年获得IEEE 控制系统协会Antonio Ruberti Young Researcher Prize，于2008年获得美国国家科学基金会杰出青年教授/杰出学术发展奖。