1. A method for the fusion of information originating from two or more sensors (C 1 , C 2 ) each producing respective belief functions (m C1 , m C2 ) representing knowledge on a reasoning space (Ω), and comprising several states (E 1 ,. .. , EN) each representing an operating state of the set of sensors, said method comprising: calculating a merged belief function (m F ); and calculating conditional belief functions on the basis of the belief functions (m C1 , m C2 ) produced by the sensors (C 1 , C 2 ) and of a predefined base of operators comprising associations between states of sensors and merge operators, wherein a conditional belief function represents knowledge of a sensor on the reasoning space (Ω) when the set of sensors is in a given operating state (E 1 ,. .. , EN), and wherein a step of calculating the merged belief function (m F ) uses the conditional belief functions calculated and knowledge arising from a knowledge base regarding the operating state of the sensors, wherein the calculating the merged belief function (m F ) is carried out using contextual information indicating that, if an object observed by the sensors (C 1 , C 2 ) is of type A, then the sensors have a propensity p to be in a set of states E′, where A is a subset of the space of propositions (Ω) and E′ is a subset of a set E of the various states (E 1 ,. .. , EN) of the sensors and wherein the merged belief function (m F ) follows the following relation: 
 m F =((⊕ i=1,. .. ,N D m Ω [Ei E×Ω )⊕ D (⊕ j=1,. .. ,K D m E [Aj] E×Ω )) ↓Ω where ⊕ D is an operator called the unnormalized Dempster's rule, m Ω [Ei], with i=1,. .. , N, are conditional belief functions representing knowledge on the reasoning space (Ω) when the sensors are in various states (E 1 ,. .. , EN), N being the number of operating states, and m E [Aj] is contextual knowledge regarding the propensity of the sensors to be in a given operating state when an object observed by the sensors is of type A j , with j=1,. .. , K, A j being a subset of Ω and such that A 1 ,. .. , A K forms a partition of Ω, ↑, ↓ and being operations for manipulating belief functions being a vacuous extension, a marginalization and a deconditioning, respectively.