Multicomponent WVD Spectrogram Enhancement Algorithm for Indoor Through-Wall Radar Target Tracking

Doppler through-wall radar (TWR) is a promising device for the Internet of Things (IoT), effective for indoor tracking, health monitoring, and smart homes. However, employing it to estimate the trajectories of multiple targets presents challenges associated with time-frequency analysis (TFA). In this article, a multimodal network called MWVD is proposed, which eliminates the crossterm problem of Wigner-Ville distribution (WVD) and improves the accuracy of instantaneous frequency (IF) extraction to obtain accurate localization. In the MWVD, both the WVD spectrogram and the 1-D complex signals are used as inputs to the network. The complex signals are passed through the proposed multiwindow short-time filtering (MWSTF) module followed by an adaptive wavelet attention fusion (AWAF) module to simulate the wavelet transform. Subsequently, the enhanced WVD spectrogram is obtained by the energy compression module. As a result, comprehensive experiments, including simulated signal tests, module ablation studies, fusion mode ablation analyses, and real TWR target tracking, are conducted to demonstrate the proposed algorithm's excellence, which will be combined with more IoT applications in the future.