A New Method for Confidence of Network Based on Fuzzy Matrix Operations

In the domain of fusion for passive signals, the confidence degrees for conversations and communication networks are critical to obtain the precise belief degree for higher level information. In this paper, we first examine the characteristics of messages, emitters, conversation, and communication network. By defining several temporal functions, we then propose the formulation of association degree, termed as homology degree, for clustering messages into emitters, and present mathematical formulation to compute the confidence degree for any possible conversation between a pair of emitters. Subsequently, by using fuzzy matrix operations, we introduce the incidence matrix for a set of ongoing conversations, and design an iterative fuzzy matrix operation to deduce the confidence matrix for a communication network. Also, by using Dempster-Shafer combination rule, we describe a method to combine two confidence matrices for a network from different sources, or at different time steps. Finally, we provide an example to show the purport and effectiveness of our methods.