Quickest change point detection with multiple postchange models

We study the sequential quickest change point detection for systems with multiple possible postchange models. A change point is the time instant at which the distribution of a random process changes. In many practical applications, the prechange model can be easily obtained, yet the postchange distribution is unknown due to the unexpected nature of the change. In this article, we consider the case that the postchange model is from a finite set of possible models. The objective is to minimize the average detection delay (ADD), subject to upper bounds on the probability of false alarm (PFA). Two different quickest change detection algorithms are proposed under Bayesian and non-Bayesian settings. Under the Bayesian setting, the prior probabilities of the change point and prior probabilities of possible postchange models are assumed to be known, yet this information is not available under the non-Bayesian setting. Theoretical analysis is performed to quantify the analytical performance of the proposed algorithms in terms of exact or asymptotic bounds on PFA and ADD. It is shown through theoretical analysis that when PFA is small, both algorithms are asymptotically optimal in terms of ADD minimization for a given PFA upper bound. Numerical results demonstrate that the proposed algorithms outperform existing algorithms in the literature.