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Grant: Discovery Grants Program - Individual

University of British Columbia

$65,294.00

Stochastic sensor scheduling

EF 2012-13

Résumé
Statistical signal processing deals with extracting signals from noisy measurements. In this project, motivated by energy, communication and computational constraints, we address the deeper issue of how to dynamically schedule and optimize signal processing resources to extract signals from noisy measurements. Such sensor scheduling/management problems involve the interplay of ideas in Bayesian signal processing, stochastic optimization, stochastic control and game theory. Our research involves four inter-related parts. In Part 1, we conduct fundamental research on the structural properties of sensor management problems formulated as constrained Markov decision processes, partially obeserved Markov decision processes and stochastic games. In Part 2, we exploit the structure in Part 1 to derive sophisticated simulation-based optimization algorithms for sensor management. In Part 3, we exploit the fundamental results in Part 1 and Part 2 to design and analyse efficient algorithms for networked sensors in surveillance and radar resource management. In Part 4, we use the fundamental results in Part 2 to devise efficient multiparticle Brownian dynamics and Molecular dynamics simulation of ion permeation in biological membrane ion channels. The key idea underpinning our research is the study of stochastic scheduling and games for dynamical systems. This unifying theme motivates our fundamental theory and applications. The outcomes of this project are twofold: (i) Novel theoretical developments and analysis in the structure of sensor scheduling policies (ii) Computationally efficient numerical algorithms and associated convergence analysis in sensor scheduling and large-scale interacting particle simulation. These algorithms will serve as useful computational tools for radar resource management, sensor networks and computational biophysics. To achieve our objectives we will use powerful analysis tools such as supermodularity, weak convergence, analysis of differential inclusions, and game theoretic ideas such as global games and correlated equilibria. Many of these fundamental tools are motivated by recent advances in the economics literature.
Programme
Discovery Grants Program - Individual
Emplacement
British Columbia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
251246-2008
Name nom
Krishnamurthy, Vikram
Department département
Electrical and Computer Engineering
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2012
Competition year année de concours
2008
Award amount
65294
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
334
Committee name en
Commun.,Computers & Components
Committee name fr
Communic.,ordin. et composants
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2514
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Digital signal processing
Research subject fr
Traitement des signaux numériques
Partie
5
Nb partie
5
Application title
Stochastic sensor scheduling
Keyword
NSERC
Application summary
Statistical signal processing deals with extracting signals from noisy measurements. In this project, motivated by energy, communication and computational constraints, we address the deeper issue of how to dynamically schedule and optimize signal processing resources to extract signals from noisy measurements. Such sensor scheduling/management problems involve the interplay of ideas in Bayesian signal processing, stochastic optimization, stochastic control and game theory. Our research involves four inter-related parts. In Part 1, we conduct fundamental research on the structural properties of sensor management problems formulated as constrained Markov decision processes, partially obeserved Markov decision processes and stochastic games. In Part 2, we exploit the structure in Part 1 to derive sophisticated simulation-based optimization algorithms for sensor management. In Part 3, we exploit the fundamental results in Part 1 and Part 2 to design and analyse efficient algorithms for networked sensors in surveillance and radar resource management. In Part 4, we use the fundamental results in Part 2 to devise efficient multiparticle Brownian dynamics and Molecular dynamics simulation of ion permeation in biological membrane ion channels. The key idea underpinning our research is the study of stochastic scheduling and games for dynamical systems. This unifying theme motivates our fundamental theory and applications. The outcomes of this project are twofold: (i) Novel theoretical developments and analysis in the structure of sensor scheduling policies (ii) Computationally efficient numerical algorithms and associated convergence analysis in sensor scheduling and large-scale interacting particle simulation. These algorithms will serve as useful computational tools for radar resource management, sensor networks and computational biophysics. To achieve our objectives we will use powerful analysis tools such as supermodularity, weak convergence, analysis of differential inclusions, and game theoretic ideas such as global games and correlated equilibria. Many of these fundamental tools are motivated by recent advances in the economics literature.