Unsupervised Domain Adaptation for Micro-Doppler Human Motion Classification via Feature Fusion

Micro-Doppler-based human motion classification has become a topical area of research recently. However, the current research is limited by the lack of labeled training data. Domain adaptation, namely, the ability to take advantage of knowledge from an available source data set and apply it to an unlabeled target data set, is useful in this situation. A typical strategy for this transfer learning technique is to extract domain-invariant feature representations. In this letter, an unsupervised domain adaptation method for micro-Doppler classification is proposed. Given no available measurement training samples, we creatively utilize the motion capture database as an auxiliary and adapt its interior knowledge to the measurement data set. To achieve domain-invariant features, three types of features are extracted and fused including low-level deep features from the convolutional neural network, empirical features, and statistical features. After feature fusion, a k-nearest neighbor classifier is applied to the measurement data to classify seven human activities. Experimental results show that our approach outperforms several state-of-the-art unsupervised domain adaptation methods. The impact of the output from different convolution layers is further investigated, and ablation studies of the efficacy of each feature are also carried out in this letter.