Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems

DOI10.1016/j.automatica.2015.08.022zbMath1327.93369OpenAlexW1803898544MaRDI QIDQ900693

Stephen L. Smith, Syed Talha Jawaid

Publication date: 22 December 2015

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2015.08.022

zbMATH Keywords

Kalman filterssensor schedulinggreedy algorithmssensor networksmulti-sensor estimation

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05)

Related Items (11)

Optimal sensor scheduling for multiple linear dynamical systems ⋮ A randomized approach to sensor placement with observability assurance ⋮ Multi-hop sensor network scheduling for optimal remote estimation ⋮ A complete greedy algorithm for infinite-horizon sensor scheduling ⋮ Maximization of nonsubmodular functions under multiple constraints with applications ⋮ Distributed strategy selection: a submodular set function maximization approach ⋮ Submodularity-based false data injection attack scheme in multi-agent dynamical systems ⋮ An exact solution approach for the mobile multi‐agent sensing problem ⋮ Sensor selection for Kalman filtering of linear dynamical systems: complexity, limitations and greedy algorithms ⋮ Optimal sensor communications in presence of transmission delays and bandwidth limitations ⋮ Sensor scheduling for linear systems: A covariance tracking approach

Cites Work

• On efficient sensor scheduling for linear dynamical systems
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• Sensor selection via compressed sensing
• On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage
• On the determination of optimal costly measurement strategies for linear stochastic systems
• An analysis of approximations for maximizing submodular set functions—I
• Sensor Selection via Convex Optimization
• Kalman Filtering With Intermittent Observations
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