Decision-Level Sensor-Fusion Based on DTRS

摘要

A decision-level sensor fusion based on decision-theoretic rough set (DTRS) model is proposed. Sensor fusion is the process of combining sensor readings from disparate resources such that the resulting information is more accurate and complete. Decision-level sensor fusion combines the detection results instead of raw data of different sensors, and it is most suitable when we have different types of sensors. Rough set theory offers a three-way decision approach to combine sensor results into three regions and reasoning under uncertain circumstances. Based on DTRS, we build a cost-sensitive sensor fusion model. A loss function is interpreted as the costs of making different classification decisions, the computation of required thresholds to define the three regions is based on the loss functions. Finally, an illustrative example demonstrates the framework's effectiveness and validity.