Power Spectrum Estimation of Random Signal Missing Data Based on Machine Learning
In order to solve the problem of irregular and incomplete data that may occur during the transmission and reception of random signals,a prediction method based on the combination of XGBoost-SVR and gated recurrent unit neural network was proposed for use missing data filling for sparse random signals.Firstly,the original dataset was filled using an extreme gradient boosting tree and support vector regression.Further the gated cyclic unit model was used to train and predict the missing data.Finally,the power spectrum estimation was achieved by Fourier transform or wavelet transform.The numerical analysis results showed that in the presence of noise interference,even if the data is missing by up to 70%,the proposed method can reproduce the target power spectrum well.The missing data processed by this method can better restore the characteristics of random sig-nals,providing strong support for signal prediction and disaster prevention.
NETL
NSTL
万方数据
维普
