A Granular Multi-Sensor Data Fusion Method for Situation Observability in Life Support Systems

the projection of such state into the near future. This paper presents a multi-sensor data fusion method that collects discrete human-inputs and measurements to generate a granular perception function that supports situation observability. These human-inputs are situation-rich, meaning they combine measurements dening the operational condition of the system with a subjective assessment of its situation. As a result, the perception function produces situation-rich signals that may be employed in user-interfaces or in adaptive automation. The perception function is a fuzzy associative memory (FAM) composed of a number of granules equal to the number of situations that may be detected by human-experts; its development is based on their interaction with the system. The human-input data sets are transformed into a granular structure by an adaptive method based on particle swarms. The paper proposed describes the multi-sensor data fusion method and its application to a ground-based aquatic habitat working as a small-scale environmental system.