  To solve the problems of reduced accuracy and prolonging convergence time of
through-the-wall radar (TWR) human motion due to wall attenuation, multipath
effect, and system interference, we propose a multilink auto-encoding neural
network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE
algorithm is jointly constructed by a singular value decomposition (SVD)-based
data preprocessing module, an improved coordinate attention module, a
compressed sensing learnable iterative shrinkage threshold reconstruction
algorithm (LISTA) module, and an adaptive weight module. The data preprocessing
module achieves wall clutter, human motion features, and noise subspaces
separation. The improved coordinate attention module achieves clutter and noise
suppression. The LISTA module achieves human motion feature enhancement. The
adaptive weight module learns the weights and fuses the three subspaces. The
TWR-MCAE can suppress the low-rank characteristics of wall clutter and enhance
the sparsity characteristics in human motion at the same time. It can be linked
before the classification step to improve the feature extraction capability
without adding other prior knowledge or recollecting more data. Experiments
show that the proposed algorithm gets a better peak signal-to-noise ratio
(PSNR), which increases the recognition accuracy and speeds up the training
process of the back-end classifiers.
