Multi-Rank Subspace Change-Point Detection for Monitoring Robotic Swarms

We study the problem of real-time detection of covariance structure changes in high-dimensional streaming data, motivated by applications such as robotic swarm monitoring. Building upon the spiked covariance model, we propose the multi-rank Subspace-CUSUM procedure, which extends the classical CUSUM framework by tracking the top principal components to approximate a likelihood ratio. We provide a theoretical analysis of the proposed method by characterizing the expected detection statistics under both pre- and post-change regimes and offer principled guidance for selecting the drift and threshold parameters to control the false alarm rate. The effectiveness of our method is demonstrated through simulations and a real-world application to robotic swarm behavior data.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-06-23

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  arXiv 2506.18562
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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