Fast and Robust State Estimation and Tracking via Hierarchical Learning
This work addresses the need for fast and reliable state estimation in Cyber-Physical Systems for tactical or civilian environments, representing an incremental improvement over existing distributed solutions.
The paper tackled the problem of slow convergence and vulnerability to communication failures in state estimation and tracking for large-scale networks by proposing two 'consensus + innovation' algorithms with a hierarchical push-sum consensus component, achieving faster convergence rates as validated through simulations.
Fast and reliable state estimation and tracking are essential for real-time situation awareness in Cyber-Physical Systems (CPS) operating in tactical environments or complicated civilian environments. Traditional centralized solutions do not scale well whereas existing fully distributed solutions over large networks suffer slow convergence, and are vulnerable to a wide spectrum of communication failures. In this paper, we aim to speed up the convergence and enhance the resilience of state estimation and tracking for large-scale networks using a simple hierarchical system architecture. We propose two ``consensus + innovation'' algorithms, both of which rely on a novel hierarchical push-sum consensus component. We characterize their convergence rates under a linear local observation model and minimal technical assumptions. We numerically validate our algorithms through simulation studies of underwater acoustic networks and large-scale synthetic networks.