Digital Signal Type Identification Using Swarm Intelligence Tuned Minimal Fuzzy Radial Basis Function Neural Network

Automatic identification of digital signal types is of interest for both civil and military applications. This paper presents an efficient signal type identifier that includes a variety of digital signals. In this method, a combination of spectral and statistical features is used as an input to the classifier. Also the features are weighted based on the degree of dispersion to increase the effect of features. A fuzzy neural network with swarm intelligence (SI) for adjustment of the parameters of the network is used as a classifier. The effectiveness of the proposed system is evaluated by comparing the results obtained by the use of models seen in the literature. Simulation results show that the proposed method has high performance for identification of different kinds of digital signal even at very low SNRs. This high efficiency is achieved with features, which have been selected using principal component analysis and network parameters using swarm optimiser.