Convolutional neural network applied to specific emitter identification based on pulse waveform images

Abstract

To deal with problems of uncertain modulations and multiple pulse widths in pulse waveforms (PWs) during theidentifying procedure, a novel specific emitter identification (SEI) method based on PW images (PWIs) and convolutional neuralnetwork is proposed. In the method, a more accurate signal model is built with considering the rising, steady and falling part ofthe whole PW based on actual radar pulse signals. PWI achieves transforming time-domain waveforms to 2D binary images asan SEI analysis feature. To match the PWI feature, a convolutional neural network with the small convolutional kernel isdesigned to extract the subtle features and finish the supervised training. By tuning the parameters of the convolutional neuralnetwork, it completes a balance of consuming time and identifying accuracy. Simulations and experiments indicate that theproposed method outperforms the existed methods on identifying radar individuals with uncertain modulations and multiplepulse widths in the intercepted pulse signals.