Multi-variate Quickest Detection of Significant Change Process

TL;DR

This paper presents a mathematical model for multivariate quickest detection of significant changes in sensor networks, focusing on Markovian signals and a game-theoretic fusion center to confirm intruder presence.

Contribution

It introduces a novel multivariate change detection framework using a non-cooperative stopping game and a fusion center model for sensor networks.

Findings

Model effectively detects transition probability changes in sensor signals.

Fusion center confirms intruder presence through coordinated detection.

Game-theoretic approach enhances detection reliability.

Abstract

The paper deals with a mathematical model of a surveillance system based on a net of sensors. The signals acquired by each node of the net are Markovian process, have two different transition probabilities, which depends on the presence or absence of an intruder nearby. The detection of the transition probability change at one node should be confirmed by a detection of similar change at some other sensors. Based on a simple game the model of a fusion center is then constructed. The aggregate function defined on the net is the background of the definition of a non-cooperative stopping game which is a model of the multivariate disorder detection.