Quickest detection of a random signal in background noise using a sensor array
EURASIP Journal on Applied Signal Processing
Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems
Computational Statistics & Data Analysis
Human activity localization via sequential change detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Communication under strong asynchronism
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Human motion analysis via statistical motion processing and sequential change detection
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Distributed detection and localization of events in large ad hoc wireless sensor networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Nonparametric sequential change-point detection by a vertically trimmed box method
IEEE Transactions on Information Theory
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
| Hi-index | 755.02 |
The purpose of this paper is to give a new statistical approach to the change diagnosis (detection/isolation) problem. The change detection problem has received extensive research attention; however, the change isolation problem has, for the most part, been ignored. We consider a stochastic dynamical system with abrupt changes and investigate the multiple hypotheses extension of Lorden's (1971) results. We introduce a joint criterion of optimality for the detection/isolation problem and then design a change detection/isolation algorithm. We also investigate the statistical properties of this algorithm. We prove a lower bound for the criterion in a class of sequential change detection/isolation algorithms. It is shown that the proposed algorithm is asymptotically optimal in this class. The theoretical results are applied to the case of additive changes in linear stochastic models