Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis

被引：0

作者：

Molloy, Timothy L. ^{[1
,2
]}

机构：

[1] Queensland Univ Technol, Brisbane, Qld 4000, Australia

[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2021年 / 66卷 / 02期

关键词：

Fault detection; Fault detection and isolation; minimax robustness; quickest change diagnosis;

D O I：

10.1109/TAC.2020.2985975

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：857 / 864

页数：8