Paper Info
Title
TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net
Abstract
The micro-motion target echoes can be regarded as the accumulation of a few strong scattering points echoes, which are naturally sparse. Therefore, the compressed sensing (CS) reconstruction method can be used to analyze the incomplete micro-Doppler signal and extract the micro-motion features. Traditional CS reconstruction algorithms are time-consuming and sensitive to the selection of model parameters, limiting the performance of micro-motion feature extraction. This paper proposes a deep unfolded method to achieve the reconstruction of the time-frequency representation (TFR) of incomplete micro-Doppler signals. First, the joint time-frequency (JTF) transform is used to construct the CS model and the Training data is obtained according to the model. Then, the alternating direction method of multipliers (ADMM) algorithm is constructed into an iterative network named ADMM-net corresponding to the iterative solution process. An auxiliary loss is designed into the ADMM-net called AL-ADMM-net to improve the quality of reconstruction. The AL-ADMM-net is trained by the training data to learn the optimal parameters. Furthermore, an AL-ADMM-net iterative method is proposed when the micro-Doppler signal has phase corruption. Simulations are given to prove the effectiveness of the proposed method.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3212739
IEEE ACCESS
Keywords
DocType
Volume
Feature extraction, Time-frequency analysis, Radar, Scattering, Image reconstruction, Deep learning, Reconstruction algorithms, Doppler radar, Compressed sensing, Compressed sensing, micro-doppler, ADMM-net, deep learning, auxiliary loss
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yanxin Yuan100.68
Ying Luo202.37
Qun Zhang312525.42
Cong Zhang414926.42