Quickest change detection in distributed sensor systems

In the conventional formulation of the change-point detection problem, there is a sequence of observations whose distribution changes at some unknown point in time, and the goal is to detect this change as quickly as possible, subject to false alarm constraints. It is known that in the case where the observations are independent and identically distributed (iid) and the change point is modeled as a geometrically distributed random variable, the Shiryaev detection procedure minimizes the expected detection delay, subject to a constraint on the false alarm probability In this paper we present effective decentralized detection procedures for the multi-sensor situation where the information available for decision-making is distributed across a set of sensors. We present asymptotically optimal procedures for two scenarios. In the first scenario, the sensors send quantized versions of their observations to a fusion center where the change detection is performed based on all the sensor messages. In the second scenario, the sensors perform local change detection using Shiryaev-Roberts procedures and send their final decisions to the fusion center for combining. We show that our decentralized procedures for latter scenario have the same first order asymptotic performance as the centralized Shiryaev-Roberts procedure that has access to all of the sensor observations. We also present numerical results for a simple example involving Gaussian observations.