An Observation Capability Information Association Model for Multisensor Observation Integration Management: A Flood Observation Use Case in the Yangtze River Basin

Sensor planners find it difficult to choose the right sensor combination for observation tasks because current sensor discovery methods can only generate lists of individual sensors without considering the association between sensors that is conveyed by understanding their observation capability information. This limitation results in the failure to properly plan sensor observations and to obtain accurate observation information. The observation capability information association model (OCIAM) is proposed. An inherent state model, dynamic observation model, and correlation model are formulated in the framework of the OCIAM. The core of OCIAM is the correlation model in which four correlation modes (competitive, complementary, enhanced, and cooperative) are illustrated. A prototype system called MultisensorAssociation is developed for constructing an experiment for flood observation in the lower reaches of the Jinsha River Basin. The results of this case study verify that the OCIAM can be used for the selection of sensors or sensor combinations and has important theoretical and practical significance for multisensor observation integration management.