UWB radar target identification based on linear RBFNN

In this paper, a radical-basis-function neural network(RBFNN) with efficient linear learning algorithm is presented for the identification on target profiles of Ultra Wideband(UWB) radar. This linear RBFNN has both good localization approximation and linear computation complexity with the number of dimension and number of inputs. Its performance is comparable with support vector machine (SVM) for tasks of pattern recognition with a rapider speed. We applied it to the identification of target profile in UWB radar, which needs excessively fast processing. The experimental results are achieved including higher recognition rate and shorter consumed time, which is superior to its counterparts.