Authors:
Felix Sawo
;
Kathrin Roberts
and
Uwe D. Hanebeck
Affiliation:
Intelligent Sensor-Actuator-Systems Laboratory, Institute of Computer Science and Engineering, Universität Karlsruhe (TH), Germany
Keyword(s):
Stochastic systems, Baysian estimation, model-based reconstruction, distributed phenomenon, environmental monitoring, sensor-actuator-network.
Related
Ontology
Subjects/Areas/Topics:
Environmental Monitoring and Control
;
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper addresses a systematic method for the reconstruction and the prediction of a distributed phenomenon characterized by partial differential equations, which is monitored by a sensor network. In the first step, the
infinite-dimensional partial differential equation, i.e. distributed-parameter system, is spatially and temporally decomposed leading to a finite-dimensional state space form. In the next step, the state of the resulting
lumped-parameter system, which provides an approximation of the solution of the underlying partial differential equations, is dynamically estimated under consideration of uncertainties both occurring in the system and arising from noisy measurements. By using the estimation results, several additional tasks can be achieved by the sensor network, e.g. optimal sensor placement, optimal scheduling, and model improvement. The performance of the proposed model-based reconstruction method is demonstrated by means of simulations.