This paper provides an overview of recent developments in quickest change detection (QCD) for high-dimensional multi-sensor systems, with an emphasis on settings involving structural constraints and limited sensing resources. Classical QCD methodologies, while well understood in low-dimensional and fully observed regimes, face significant challenges when extended to modern applications characterized by large-scale data, constrained sampling or communication, and heterogeneous signal structures. We review key approaches for handling high dimensionality, including methods that exploit sparsity, and other forms of signal heterogeneity. Additionally, we discuss sampling constraints, where observations must be selected or acquired sequentially under resource limitations. Multi-stream applications can require making multiple detections, for example when detecting changes separately in different streams. The underlying assumptions on probability models, the types of changes taking place, commonly used decision-making criteria, performance indices, and error types are described. We also briefly discuss the application of machine learning in cases where the underlying probability models are not known or there is a need to select which sensors should monitor the phenomena because of the large scale of the system.

翻译：本文综述了高维多传感器系统中快速变化检测的最新进展，重点关注涉及结构约束和有限传感资源的场景。经典快速变化检测方法在低维和完全可观测场景中已得到充分理解，但当扩展至以大规模数据、采样或通信受限以及异质信号结构为特征的现代应用时，面临显著挑战。我们回顾了处理高维性的关键方法，包括利用稀疏性和其他信号异质性形式的途径。此外，我们讨论了采样约束，即需在资源限制下顺序选择或获取观测值的情况。多流应用可能需要执行多次检测，例如在不同流中分别检测变化。本文描述了关于概率模型的基本假设、变化类型、常用决策准则、性能指标及误差类型。我们还简要讨论了在基础概率模型未知或因系统规模庞大需选择哪些传感器监测现象时，机器学习技术的应用。