Symbol Rate Estimation Based on Convolutional Neural Network
In electronic reconnaissance,the symbol rate of target signal is an important parameter,and acquiring this parameter is a prerequisite for further demodulation and interpretation of the signal.There-fore,symbol rate estimation is of great practical significance.In recent years,with the rapid development of artificial intelligence technology,deep learning has been widely applied in the field of signal processing This article proposes a symbol rate estimation method based on convolutional neural networks,which di-rectly takes the sampled data of target signal as network input to obtain the corresponding estimation re-sults.This method does not require prior knowledge of signal modulation style,and the estimation process for signals with different modulation styles is consistent.So the algorithm process has been simplified.The effectiveness of the algorithm is verified by simulation.The simulation results show that this method can estimate the signal symbol rate accurately and has good adaptability to short burst signals and low sig-nal-to-noise ratio scenarios.
deep learningconvolutional neural networkssymbol rate estimation
万方数据
维普
