  The radar signal processing algorithm is one of the core components in
through-wall radar human detection technology. Traditional algorithms (e.g.,
DFT and matched filtering) struggle to adaptively handle low signal-to-noise
ratio echo signals in challenging and dynamic real-world through-wall
application environments, which becomes a major bottleneck in the system. In
this paper, we introduce an end-to-end through-wall radar human detection
network (TWP-CNN), which takes raw radar Analog-to-Digital Converter (ADC)
signals without any preprocessing as input. We replace the conventional radar
signal processing flow with the proposed DFT-based adaptive feature extraction
(DAFE) module. This module employs learnable parameterized 3D complex
convolution layers to extract superior feature representations from ADC
signals, which is beyond the limitation of traditional preprocessing methods.
Additionally, by embedding phase information from radar data within the network
and employing multi-task learning, a more accurate detection is achieved.
Finally, due to the absence of through-wall radar datasets containing raw ADC
data, we gathered a realistic through-wall (RTW) dataset using our in-house
developed through-wall radar system. We trained and validated our proposed
method on this dataset to confirm its effectiveness and superiority in real
through-wall detection scenarios.
