Distributed Filtering for Cyber-Physical Systems: Consistency, Stability and Security
| In this talk, the distributed filtering problem over cyber-physical systems (CPS) is investigated. First, a scalable and fully distributed Kalman filter is provided and analyzed to achieve the consistent estimation for the states of potentially unstable systems. Then, we study the case with state equality constraints (SEC). We propose a distributed Kalman filter with guaranteed consistency and satisfied SEC Then, we establish the relationship between consensus step, SEC and estimation error covariance in dynamic and steady processes, respectively. Under an extended collective observability condition based on SEC, the stability of the filter is studied. Moreover, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. We first propose a recursive distributed filter consisting of saturation-like observation update and consensus operation of state estimates. A sufficient condition is then established for the boundedness of estimation error.