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.

It is an important topic to effectively perceive the gestures of the elderly and patients, communicate conveniently with the deaf and dumb, and accurately transmit abnormal alarm information through the network, which would have vast applications in the future. Seamlessly communicating among people, including vulnerable groups, is an important sign of social civilization and technological progress. As an important sensor, micro-Doppler (MD) radar has many advantages such as not being restricted by ambient light, long detection distance, no need to consider occlusion, and sensitive detection to slight movements. In recent years, it has been used in various recognition tasks. In this article, we proposed a sign language (SL)/hand gesture recognition method based on a novel discriminative feature, built a measurement system of hand movements using an ultrawideband (UWB) radar, measured ten-type, 15-type SL actions, and ten-type hand gesture actions, and completed the tasks of ten-type SL/hand gesture recognition based on the extracted new features. Compared with the five related methods, experimental results show that the proposed method improves the recognition accuracy by 6.4% on the ten-type SL dataset, which proves the effectiveness of the new cumulative distribution density (CDD) features. For comparison, the proposed method is directly applied to the ten-type hand gesture dataset without any change, and recognition accuracy is improved by 8.6%, which verifies the universality of the proposed new CDD features. Meanwhile, we also give the experimental results when the radial distance measured by radar changes and the types of SL increase.

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.

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons' motions in a small-scale target data set, ITL can also classify the persons' motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.

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.