Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis

• 成果类型: Article
• 署名作者: Molloy, Timothy L.
• 署名单位: Queensland University of Technology (QUT); University of Melbourne
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2985975
• 发表日期: 2021
• 页码: 857-864
• 关键词: Fault detection; fault detection and isolation; minimax robustness; quickest change diagnosis

摘要

The problem of quickly diagnosing an unknown change in a stochastic process is studied. We establish novel bounds on the performance of misspecified diagnosis algorithms designed for changes that differ from those of the process, and pose and solve a new robust quickest change diagnosis problem in the asymptotic regime of few false alarms and false isolations. Simulations suggest that our asymptotically robust solution offers a computationally efficient alternative to generalised likelihood ratio algorithms.