Chapter of CSS/CASS/SMCS Presents
DATE: March 23, 2021
TIME: 7:00-8:00 PM ET
TOPIC: Distributed dynamic state estimation with networked agents: Consistency, confidence, and convergence
Click HERE to register for webinar.
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 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 distributed hybrid information fusion 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.