Learning-Based Sparse Sensing With Performance Guarantees

• 成果类型: Article
• 署名作者: Vafaee, Reza; Siami, Milad
• 署名单位: Northeastern University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3424368
• 发表日期: 2025
• 页码: 387-402
• 关键词: Observability; vectors; estimation; schedules; Robot sensing systems; Linear systems; Eigenvalues and eigenfunctions; Near-optimal approximation; nonsubmodularity; observability; Regret minimization; sensor networks; time-varying scheduling

摘要

In this study, we address the challenge of sensor scheduling in discrete-time linear dynamical networks. We propose a novel learning-based rounding method aimed at converting a provided weighted sensor schedule into a sparse, unweighted schedule while preserving a comparable level of observability performance to the original weighted schedule. We introduce the notion of L-systemic performance measures, which enjoy characteristics such as homogeneity, monotonicity, convexity, and Lipschitz continuity, covering a range of well-known measures. We integrate the initialization of the weighted sensor schedule, achieved via a convex relaxation of a combinatorial optimization problem based on an L-systemic measure, into our rounding approach. We show that this produces an unweighted sensor schedule that achieves a (1 + & varepsilon;) near-optimal approximation solution while ensuring system observability. Our polynomial-time deterministic framework provides a performance guarantee compared to the optimal solution for all types of L-systemic performance measures, including a class of nonsubmodular metrics. The effectiveness of the theoretical findings is evaluated for a benchmark numerical example in distributed frequency control.