Monitoring distributed parameter systems is a complex task, and requires a large number of sensors. Fortunately, the emergence of increasingly inexpensive and capable Unmanned Aerial Vehicles (UAVs) enables efficient monitoring of extended areas with a limited number of sensors. In this chapter, we focus on model-based state estimation when UAVs equipped with mobile sensors are used to sequentially estimate a system of high dimensionality. We compare performance of the classical Kalman filter with its variants in constructing probabilistic maps of air pollution transport. We test various sensor configurations including a combination of fixed and UAVs equipped with mobile sensors, and present an empirical analysis of the value added by mobile sensing. We describe the use of consensus Kalman filter for cooperative estimation in the case of a team of UAVs, formulate an information-aware path planning algorithm for UAVs, and illustrate the importance of UAV path planning on simulation data.
Improving Distributed Parameter System State Estimation Using UAV-Based Mobile Sensors: Application in Air Pollution Monitoring
synth.
2023-06-30
18 pages
Article/Chapter (Book)
Electronic Resource
English
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