As a typical task of passive biometrics, behavior-based person identification has been studied extensively in recent years. This letter proposes the use of the ultrawideband impulse radar for person identification based upon the micro-Doppler signatures of human motions. A new convolutional neural network (CNN) architecture is proposed for taking advantage of the hierarchical features. The experimental results show that, by utilizing the micro-Doppler signatures of the six selected human motions, the task of person identification can be accurately achieved. Both traditional algorithms and landmark CNN algorithms are chosen for comparison, and the proposed model performs better than the others. Especially when the motion of “running” is adopted to identify persons, the model achieves 95.21% accuracy on the identification of 15 people.

In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is essential for spectral analysis. Thus, a micro-Doppler-based classifier is considered to be ``angle-sensitive.'' In this article, we propose an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network. We further provide a sensible definition of ``angle sensitivity,'' and perform experiments on two data sets obtained through simulations and measurements. The results demonstrate that the proposed algorithm outperforms both feature-based and existing deep-learning-based counterparts, and resolve the issue of angle sensitivity in micro-Doppler-based classification.

In a smart home environment, assisted living has been a topic of great research over the past decade. Human motion analysis is considered as a key technology for living states recognition in an assisted living system. Recent research has proved that rich information can be obtained from human movements, such as the motion category, moving patterns, and human identity. In this paper, a non-intrusive human movement sensing system is established with a mono-static ultra-wide bandwidth radar. Then, we propose a well-designed joint motion classification and person identification convolutional neural network (named as ‘JMI-CNN’). To recognize human motions and identities simultaneously, the network employs a multi-task learning scheme as well as the attention mechanism and the hierarchical feature reuse strategies. We report the experimental result on the data from 15 individuals, each performing six motions. It shows that the model achieves a promising performance of 80.57% on the joint task, while the accuracy for motion classification and person identification are 98.50% and 80.92%, respectively. Moreover, we carry out ablation studies to evaluate the design principles of the proposed method. Discussions on the impact of signal noise ratio and slow-time window length are also conducted.

Unlike face recognition, gait recognition does not require the cooperation of human targets, making it an attractive research area in both academia and industry. While there has been extensive research in recent years on the feasibility of using radar micro-Doppler signatures for gait recognition, little research has been conducted on how to deal with unknown target detection in micro-Doppler-based gait recognition problems, referred to as “open-set” gait recognition. In this case, it is probable that the unknown samples will resemble known samples, hence increasing the likelihood of misclassification. In this article, we focus on the problem of multiscenario open-set gait recognition (MSOGR) and present a new open-set classifier that minimizes misclassification in open-set recognition. We create a compact representation space to reduce false negatives (FNs) with a supervised contrastive learning method and propose an ensemble-based out-of-distribution (OOD) detection module so that false positives (FPs) can be alleviated at both the pixel and semantic levels. We perform the radar measurements in eight different walking manners and construct a micro-Doppler-based multiscenario gait dataset to further evaluate the proposed method’s performance. The results reveal that the proposed method outperforms the previous state-of-the-art (SOTA) open-set models, demonstrating its great potential for solving the radar-based MSOGR problem. This research would facilitate gait recognition in complex scenarios and make noncontact sensing applicable to industrial security.

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.