Performance Analysis of Information Fusion Method based on Bell Function

Multi-node and multi-feature fusion is an important approach for digital modulations signal recognition in modern communication field. Information obtained from multi-node and multi-feature needs to be fused because of incompleteness of single feature and uncertainty of single node. As a powerful method for data fusion and conclusion reasoning in uncertain environments, evidence theory is widely used. Establishing reliable BPA is the prerequisite for evidence fusion. In this paper, in order to improve the coincidence of basic probability assignment (BPA) with real probability, the notion of bell function (Bell-F) based similarity evaluation model (SEM) is introduced. Through comparative experiments, it is proved that the new method based on Bell-F is effective for BPA acquisition. Furthermore, a new information fused based digital signal modulation recognition scheme is described. Finally, a case study is given to illustrate the performance of the proposed model. Through test and calculations under the digital modulation signal data set, under 0 ~ 10dB , the recognition rate based on the Bell-F fusion method is above 90%, which is 20% higher than methods without fusion. Under 0dB or less, the integrated recognition rate of the Bell-F is 40% higher than the Gray relation method.