Automatic classification of multiple signals using 2D matching of magnitude–frequency density features

Signal classification is an important function of modern communication systems in software defined radio applications. The ability to quickly recognize the type of received signals allows a system to automatically adapt the processor to properly decode the signals. Many classification techniques assume that the received signal space is occupied by only one signal, and that the frequency of operation is known. However, in some systems, the receiver may be completely blind to the number and characteristics of signals within the bandwidth of interest. The technique introduced in this study proposes the collapsing of localized magnitude peaks from consecutive short time discrete fourier transform bins into magnitude histograms to create a two dimensional image of the frequency?Cmagnitude density of the received signal space. This image can be a useful visualization tool in the characterization of the signal space in user assisted modes of classification. Alternatively, the process could be automated by utilizing pattern recognition and image processing algorithms. Automatic signal classification is explored in this study.