Complex Convolutional Network-Based Time-Frequency Enhancement Algorithm for Target Tracking Using Doppler Through-Wall Radar

Doppler through-wall radar (TWR) is a cost-effective device to track targets in complex environments. However, when there are multiple targets in the detection area and their instantaneous frequencies (IFs) are closely arranged, mutual interference between echo components can severely degrade radar detection performance. This phenomenon is called the frequency ambiguity issue. To address it, this article proposes a time-frequency enhancement algorithm based on a complex convolutional neural network (CNN) and designs two kinds of training datasets and an improved loss function for the model. The proposed algorithm takes complex spectrograms obtained through short-time Fourier transform (STFT) as input, and the final optimized spectrogram is primarily generated through the proposed energy rearrangement module (ERM) and deep jump convolution module (DJCM). The ERM, similar to the synchro-squeezing transform (SST), condenses the energy along the frequency axis of the spectrogram using 1-D complex convolutions. Through comparative analysis of both simulation signal and TWR target tracking experiments, the algorithm is shown to effectively improve the time-frequency resolution (TFR) and suppress frequency ambiguity issue. It achieves a reduction of 57.46% in localization deviation and a decrease of 66.17% in IF deviation compared to the current state of the art.