Fast and Robust State Estimation and Tracking via Hierarchical Learning

• 成果类型: Article
• 署名作者: Mclaughlin, Connor; Ding, Matthew; Erdogmus, Deniz; Su, Lili
• 署名单位: Northeastern University; University of California System; University of California Berkeley
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3620622
• 发表日期: 2026
• 关键词: CONSENSUS

摘要

Fast and reliable state estimation and tracking are essential for real-time situation awareness in cyber-physical systems 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 article, 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.