TY - JOUR
T1 - MIMO Through-the-Wall Radar Micro-Doppler Signature Augmentation Method Based on Multi-Channel Information Fusion
AU - Gao, Weicheng
AU - Liu, Shui
AU - Wang, Jinshuo
AU - Qu, Xiaodong
AU - Yang, Xiaopeng
N1 - Publisher Copyright:
© 1994-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - Through-the-wall radar (TWR) can monitor and analyze the motion characteristics and activity patterns of indoor human targets, with the advantages of non-contact, high flexibility and privacy protection. However, existing TWR human activity recognition (HAR) techniques developed based on single-channel radar contain limited Doppler information, making it difficult to achieve accurate recognition on data where the direction of human motion is not parallel to the radar observation. To solve this problem, in this letter, a multi-input-multi-output (MIMO) TWR micro-Doppler signature augmentation method based on multi-channel information fusion is proposed. First, a multi-channel Doppler profile feature fusion method based on multi-scale wavelets with low-rank decomposition is presented. Then, a motion parameter estimation method based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) global optimization is proposed, and the fused Doppler profile transformation is implemented using the obtained orientation of human motion. Numerical simulated and measured experiments demonstrate the effectiveness of the proposed method.
AB - Through-the-wall radar (TWR) can monitor and analyze the motion characteristics and activity patterns of indoor human targets, with the advantages of non-contact, high flexibility and privacy protection. However, existing TWR human activity recognition (HAR) techniques developed based on single-channel radar contain limited Doppler information, making it difficult to achieve accurate recognition on data where the direction of human motion is not parallel to the radar observation. To solve this problem, in this letter, a multi-input-multi-output (MIMO) TWR micro-Doppler signature augmentation method based on multi-channel information fusion is proposed. First, a multi-channel Doppler profile feature fusion method based on multi-scale wavelets with low-rank decomposition is presented. Then, a motion parameter estimation method based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) global optimization is proposed, and the fused Doppler profile transformation is implemented using the obtained orientation of human motion. Numerical simulated and measured experiments demonstrate the effectiveness of the proposed method.
KW - Through-the-wall radar
KW - human activity recognition
KW - micro-Doppler signature
KW - multi-input-multi-output
UR - https://www.scopus.com/pages/publications/105027981283
U2 - 10.1109/LSP.2026.3652951
DO - 10.1109/LSP.2026.3652951
M3 - Article
AN - SCOPUS:105027981283
SN - 1070-9908
VL - 33
SP - 579
EP - 583
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
ER -