Adaptive neural networks-fuzzy reasoning information fusion system

This paper presents an adaptive neural networks-fuzzy reasoning information fusion system which is employed to decrease the influence of the uncertainty of sensor state on fusion performance under a complex environment. The model consists of a confidence estimator, fusing weight knowledge base and weighted fusion. When utilizing the new model, we can obtain the confidence of each sensor and the reliable fusion data with an adaptive synthetical computation of sensor state, sensor detection precision and track filtering error based on the techniques of neural networks, fuzzy reasoning and knowledge base. The simulation results show that the new fusion model is obviously advantageous compared with the conventional Kalman weighted fusion.