Joint time–frequency mask and convolutional neural network for real-time separation of multipath in GNSS deformation monitoring

Abstract

As one of the leading technologies of real-time dynamic deformation monitoring, global navigation satellite system (GNSS) has been widely used in deformation monitoring. Multipath error is the primary error source that limits the application of GNSS high-precision deformation monitoring, which cannot be eliminated by the double-differenced technique. It is a problem to separate and mitigate multipath in the GNSS deformation monitoring series in real-time. Therefore, we propose an approach to solve this problem, named time–frequency mask and convolutional neural network (TFM–CNN). The specific processes are as follows: (1) TFM–CNN network construction. We add a full-band deformation to the multipath and obtain the spectrogram of the signal by the short-time Fourier transform (STFT); meanwhile, the ideal ratio mask (IRM) is used to obtain the corresponding time–frequency mask matrix based on the spectrogram; furthermore, one-dimensional CNN mines the mapping relationship between the spectrogram and the time–frequency mask matrix. (2) Multipath separation. We obtain the spectrogram of the GNSS real-time deformation monitoring series by STFT. Then, we estimate its time–frequency mask matrix by the network. The matrix is multiplied by the spectrogram of the monitoring series to obtain the spectrogram of the multipath. Finally, we perform the inverse STFT to obtain the multipath in the GNSS monitoring series. The experimental results show that by training GNSS data in only a specific direction (such as the north direction), the mapping relationship between the spectrogram of multipath and the time–frequency mask matrix can be obtained, which can effectively separate multipath of the GNSS monitoring series in different observation environment in real-time. TFM-CNN significantly improves the monitoring accuracy and achieves millimeter level dynamic deformation monitoring.