Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.
['Tobias Ritter', 'Juliane Euler', 'Stefan Ulbrich', 'Oskar von Stryk']
I am writing a research paper on a idea Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes.
[{"from": "human", "value": "I am writing a research paper on a idea Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance."}]