In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusioa Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural

networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference

knowledge base and weighted fusion sectioa The simulation result demonstrates that the proposed fusion model

and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the

conventional Kaiman weighted fusion algorithm.

color:#000000, font-family:", sans serif", ,tahoma,verdana,helvetica, font-size:12px, font-style:normal, font-weight:400, text-decoration:none, ">uncertain information, information fusion, neural networks, fuzzy inference, robust estimate