Data fusion using fuzzy measures and Genetic Algorithms

This paper proposes an improvement on the fusion method presented in references [2] and [3]. In those methods not only the reliabilities of the sensors are not considered but also the choice of parameter k is relevant to the number of sensors and whether there is opinion close to 0.5. In our method Genetic Algorithms (GA) is used to find the optimal values for the reliabilities of sensors and fuzzy inference rules for determining the parameter k in multi-sensor fusion. Multi-step fusion and one-step fusion methods are formed based on the fusion functions in [2] and [3]. Simulation results show the effectiveness of the proposed methods.