An End-to-End Approach for Rigid-body Target Micro-Doppler Analysis based on the Asymmetrical Autoencoding Network

• Published in: IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
• Main Authors: Dai, Fengzhou, Liu, Jiang, Tian, Long, Dong, Hang, Hong, Ling
• Format: Article
• Language: English
• Subjects: Analysis; Asymmetrical autoencoding; Asymmetry; Deep Learning; Doppler effect; Doppler sonar; Echoes; Energy; Feature extraction; Frequency analysis; Frequency dependence; Frequency distribution; Incidence angle; Micro-Doppler Analysis; Modules; Occlusion; Radar; Radar clutter; Radar countermeasures; Radar echoes; Radar scattering; Rigid-body target; Scattering; Testing time; Time-frequency analysis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3255863

Micro-Doppler analysis of rigid-body target is significant for attitude estimation and recognition of space objects. The traditional micro-Doppler analysis method for rigid-body targets includes two sequential steps. First, the time-frequency analysis is performed on the radar echo data of the target, and then the micro-Doppler curve of each scattering center is separated and extracted from the time-frequency map. The second step depends on the micro motion model of the target. In the micro-Doppler analysis of real rigid-body target, there are some problems such as the mismatch of the micro motion model, the incidence angle dependence of the scattering center position, and the partial occlusion of the scattering center. Therefore, it is very difficult to correctly extract the micro-Doppler curves of multiple scatterers. In this paper, an end-to-end micro-Doppler analysis method for rigid-body target based on deep learning network is proposed, which can directly separate and extract the micro-Doppler curves of multiple scatterers from the target echo data. Specifically, an Asymmetrical AutoEncoding (A2E) network equipped with a Data Pre-Processing (DP2) module is developed to extract Time-Frequency Curves (TFCs) from radar echos. Considering the sparseness of Time-Frequency Distribution (TFDs), we then develop a novel Energy-Concentration Objective (ECO) function based on Min-Max game to enhance curves energy while suppress the background energy. In practice, measured TFDs are rarely annotated, it restricts the generalization capability of the A2E from the simulation to the measurement. To bridge the gap, two-fold modifications are finally constructed: i) we insert a partial-shared branch of decoder to reconstruct the TFD from the DP2 module; ii) we regularize the ECO function with a knowledge preservation based reconstruction bound to further capture the characteristics of measured echos in a semi-supervised way at test time to relieve the domain-shift problem.