An LSTM Approach to Short-range personnel recognition using Radar Signals
Zhenghui Li (University of Glasgow)
Julien Le Kernec (University of Glasgow)
F. Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Olivier Romain (CY Cergy Paris University)
Lei Zhang (University of Glasgow)
Shufan Yang (University of Glasgow)
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Abstract
In personnel recognition based on radar, significant research exists on statistical features extracted from the micro-Doppler signatures, whereas research considering other domains and information such as phase is less developed. This paper presents the use of deep learning methods to integrate both phase and magnitude features from range profiles and spectrogram. The temporal features of both domains are separately extracted using a stack of Long Short Term Memory (LSTM) layers. Then, the extracted features are aggregated in the corresponding domains and pass through a series of dense layers with SoftMax classifier. Finally, the information from the two domains is fused with a soft fusion approach to improve the performance further. Preliminary results show that the proposed network with soft fusion can achieve 85.5% accuracy in personnel recognition with six subjects