Sequential change point detection for multivariate autocorrelated data is a
very common problem in practice. However, when the sensing resources are
limited, only a subset of variables from the multivariate system can be
observed at each sensing time point. This raises the problem of partially
observable multi-sensor sequential change point detection. For it, we propose a
detection scheme called adaptive upper confidence region with state space model
(AUCRSS). It models multivariate time series via a state space model (SSM), and
uses an adaptive sampling policy for efficient change point detection and
localization. A partially-observable Kalman filter algorithm is developed for
online inference of SSM, and accordingly, a change point detection scheme based
on a generalized likelihood ratio test is developed. How its detection power
relates to the adaptive sampling strategy is analyzed. Meanwhile, by treating
the detection power as a reward, its connection with the online combinatorial
multi-armed bandit (CMAB) problem is formulated and an adaptive upper
confidence region algorithm is proposed for adaptive sampling policy design.
Theoretical analysis of the asymptotic average detection delay is performed,
and thorough numerical studies with synthetic data and real-world data are
conducted to demonstrate the effectiveness of our method.

基于状态空间模型和自适应采样策略的自适应上置信域方案用于部分可观测多传感器顺序变点检测，并通过广义似然比检验开发变点检测方案，理论分析了其侦测能力与自适应采样策略的关联，并通过合成数据和真实数据的数值研究证明了方法的有效性。