Unique in its focus, this book outlines optimal sensor placement strategies for parameter identification in dynamic distributed systems modeled by partial differential equations. The author focuses on practical applications, particularly in environmental processes, as well as on the development of new techniques and algorithms, and methods that have been successful in the related fields of optimum experimental design.
Optimal Measurement Methods for Distributed Parameter System Identification places emphasis on determining the best way of guiding moving and scanning sensors, and of making the solutions independent of the parameters being identified. The text includes extensive numerical results that show the efficiency of the proposed algorithms. Case studies involving the design of air quality-monitoring networks and network design for groundwater pollution problems are also presented to show the strength of the proposed approach in studying practical problems.
For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.
Optimal Measurement Methods for Distributed Parameter System Identification discusses the characteristic features of the sensor placement problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, culminating in the most comprehensive and timely treatment of the issue available. By presenting a step-by-step guide to theoretical aspects and to practical design methods, this book provides a sound understanding of sensor location techniques.
Both researchers and practitioners will find the case studies, the proposed algorithms, and the numerical examples to be invaluable. This text also offers results that translate easily to MATLAB and to Maple. Assuming only a basic familiarity with partial differential equations, vector spaces, and probability and statistics, and avoiding too many technicalities, this is a superb resource for researchers and practitioners in the fields of applied mathematics, electrical, civil, geotechnical, mechanical, chemical, and environmental engineering.
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