Presentation and description of two classification methods using data fusion based on sensor management

Abstract The application of multi-sensor fusion, which aims at recognizing a state among a set of hypotheses for object classification, is of major interest with regard to the performance improvement brought by the sensor complementarity. Nevertheless, this needs to take into account the most accurate information and take advantage of the statistical learning of the previous measurements acquired by sensors. When previous learning is not representative of real measurements provided by the sensors, the classical probabilistic fusion methods lack performance. The Dempster–Shafer theory is then introduced to face this disadvantage by integrating further information which is the context of the sensor acquisitions. In this paper, we propose a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as probability mass. These two methods are based on the theory of fuzzy events. Simulations of typical cases are developed in order to define the respective validity domains of these two methods. Afterwards, we are interested in the development of these two methods in the case where the previous learning is unavailable for an object and a global method of contextual information integration can be deduced.