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Sensor network scheduling for identification of spatially distributed processes

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

Since for distributed parameter systems it is impossible to observe their states over the entire spatial domain, the question arises of where to locate discrete sensors so as to estimate the unknown system parameters as accurately as possible. Both researchers and practitioners do not doubt that making use of sensors placed in an `intelligent’ manner may lead to dramatic gains in the achievable accuracy of the parameter estimates, so efficient sensor location strategies are highly desirable. In turn, the complexity of the sensor location problem implies that there are very few sensor placement methods which are readily applicable to practical situations. What is more, they are not well known among researchers. The aim of the talk is to give account of both classical and recent original work on optimal sensor placement strategies for parameter identification in dynamic distributed systems modelled by partial differential equations. The reported work constitutes an attempt to meet the needs created by practical applications, especially regarding environmental processes, through the development of new techniques and algorithms or adopting methods which have been successful in akin fields of optimal control and optimum experimental design. While planning, real-valued functions of the Fisher information matrix of parameters are primarily employed as the performance indices to be minimized with respect to the positions of pointwise sensors. Extensive numerical results are included to show the efficiency of the proposed algorithms. A couple of case studies regarding the design of air quality monitoring networks and network design for groundwater pollution problems are adopted as an illustration aiming at showing the strength of the proposed approach in studying practical problems.

This talk is part of the Isaac Newton Institute Seminar Series series.

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