As a non-intrusive sensing approach, the gait-based human identification technique attracts extensive attention. For the gait-based human identification technique, the unique gait feature is captured and extracted. Owing to the strong environment robustness and good privacy protection, the radar, especially the single-input multiple-output (SIMO) Doppler radar, is proposed as a promising way to capture the gait feature. However, the existing SIMO Doppler radar-based methods require the person to walk along a straight-line trajectory, which hinders their practical application. In this paper, we propose a gait-based human identification system for the weakly constrained trajectory, called CovertEye. In CovertEye, the person can be identified, when he/she walks along variable directions. To this end, we propose a trajectory segmentation algorithm to divide the trajectory into many straight-line trajectory segments. Based on the trajectory segments, we design the gait-based human identification method. In particular, we propose a normalization method to eliminate the differences in the direction of movement and the length among trajectory segments. The normalized signal spectrogram is exploited for the deep learning based feature extraction and human identification. We develop a prototype of the CovertEye system. The extensive experimental results demonstrate that our proposed system can achieve the identification accuracy of 82:4%.

In recent years, various intelligent activity recognition systems have been developed based on radio frequency signals such as radar, Wi-Fi, and radio frequency identification (RFID). When only one target is present, these systems can often provide high accuracy in recognizing different activities. However, such activity identification systems often fail to work due to signal interference when multiple targets coexist. To address this problem, we propose a multitarget gesture recognition system, named SoDar, based on a commercial single-input multi-output (SIMO) dual-channel Doppler radar. First, we employ endpoint detection, low-pass filtering, and discrete wavelet transform for data preprocessing. Then, we design a multitarget signal separation algorithm by maximizing the signal-to-noise ratio, and further refine the obtained signal based on principle component analysis. Afterward, we put forward a two-stage feature extraction method to extract both static and dynamic features from each separated signal. Finally, a classification model is trained to recognize the gestures of multiple targets. To verify the performance of SoDar, we selected nine different combinations of six gestures for two targets and collected more than 8000 data samples. Experimental results showed that the accuracy of two-target gesture recognition is above 90%.