UWB Spectrum Signal Recognition Based on Deep Convolutional Network
Currently,the frequency spectrum of ultra-wideband signals is irregular and the signal strength is low,thus making it easy to be interfered with by other signals,which brings serious interference to the recognition of ultra-wideband spectrum signals.In order to accurately recognize the ultra-wideband spectrum signal,this paper put forward a method of identifying ultra-wideband spectrum signals based on deep convolutional neural network.Firstly,we filtered the original ultra-wideband spectrum signal through morphological filtering,thus obtaining the noise spec-trum.Then,we adopted the classical threshold to identify the interference frequency threshold and delete the noise spectrum,thus reconstructing the ultra-wideband spectrum signal.Next,we superimposed multiple frames of ultra-wideband spectrum data and extracted the weak signal features.Finally,we inputted the superimposed image into a deep convolutional neural network for signal recognition.Experimental results prove that the proposed method has a small normalized mean square error.And the recognition rate of frequency spectrum signal is more than 90%.
Deep convolutional networkUltra-wideband UWBSpectrum signalDistinguish
万方数据
维普
