Stochastic sensor scheduling and optimization

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Several statistical signal processing applications (e.g. radar systems and civilian sensor networks) require dynamic allocation of a finite amount of resources  due to physical, communication or computational constraints. In this project we address the issue of how to dynamically schedule sensors and control signal processing resources to extract signals from noisy measurements.  This project comprises of three inter-related parts: (i) Development and analysis of novel stochastic scheduling and optimization algorithms using structural results in dynamic programming and simulation based optimization (ii)Applications of these algorithms and analysis in radar resource management and sensor networks. (iii) Applications of these simulation based stochastic optimization algoritms in Brownian dynamics and Molecular dynamics simulation of  biological membrane ion channels. The outcomes of this project are novel algorithms and analysis that permit the design of computationally efficient high performance sensor management algorithms and efficient stochastic simulation algorithms for ion channels.