Owing to the ability of automatic feature extraction, deep learning is extensively applied in human activity recognition (HAR) based on radar. In this paper, a recognition method based on multi-spectrogram and deep learning model is proposed. The radar echo data are transformed into spectrograms with different feature expressions by performing three time-frequency analyses, including short-time Fourier transform (STFT), reduced interference distribution with Hanning kernel (RIDHK) and smoothed pseudo Wigner-Ville distribution (SPWVD), and then the spectrograms are sent to deep learning model to realize HAR. The model employs three long short-term memory (LSTM) networks to learn the temporal features of the spectrograms, and uses three-dimensional convolutional neural network (3DCNN) to extract the independent spatial features of single spectrogram and the spatial correlation features between different kinds of spectrograms, named parallel LSTM-CNN network (PLCN). PLCN is capable of effectively improving the degree of feature utilization in the spectrogram and the accuracy of human activity classification by fusing both temporal and spatial features. The final experimental results show that the average recognition accuracy of the method based on PLCN for eight human activities reaches 94.75%, which is significantly higher than that of the three parallel LSTMs and the single 3DCNN.

According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. In the designed parallelism LSTM framework, multiple parallel LSTM sub-networks are trained separately to extract different temporal features from the spectrogram of each frequency and produce corresponding classification probabilities. At the decision level, the probabilities of activity classification from these sub-networks are fused by addition as the recognition output. To validate the proposed method, an experimental data set is collected by using an SFCW radar to monitor 11 participants who continuously perform six activities in sequence with three different transitions and random durations. The validation results demonstrate that the average accuracies of the designed parallelism unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) based on five frequency spectrograms are 85.41% and 96.15%, respectively, outperforming traditional Uni-LSTM and Bi-LSTM networks with only a single-frequency spectrogram by 5.35% and 6.33% at least. Additionally, the recognition accuracy of the parallelism LSTM network reveals an upward trend as the number of multi-frequency spectrograms (namely the number of LSTM subnetworks) increases, and tends to be stable when the number reaches 4.

Non-line-of-sight (NLOS) target detection utilizing multipath plays an important role in anti-terrorism, urban warfare, indoor rescue and intelligent driving. In this paper, an imaging method based on single-channel ultra-wideband (UWB) radar and dual-view observation is proposed for detecting NLOS targets. First, the cause of producing ghosts from a single view in a previous study is analyzed in detail. On this basis, with focusing on dual-view observation, the ghost problem is handled at two levels. From the level of the radar echo, path partition is conducted with the utilization of dual-view observation, solving the problem of uneven energy among multipaths. After that, two images are generated by multipath imaging, respectively, which reduces the accumulation of ghosts. From the level of the image, owing to the distinct distribution of ghosts between the imaging result, the ghosts at different locations are eliminated by image fusion. Experimental results demonstrate that compared with single view and direct multipath imaging, the approach based on dual-view observation successfully eliminates most ghosts while retaining the targets in both single-target and double-targets scenarios, which verifies the effectiveness of the method.

Radar performs target detection by transmitting electromagnetic waves and reflecting them on obstacles. It is widely utilized in military, rescue and other occasions because of the advantages beyond visual range and not restricted by lighting conditions. Since electromagnetic waves have different penetrating capabilities in different frequency bands, it is possible to penetrate the wall to image the life target on the other side of the wall by selecting the appropriate frequency band. When multiple micro-moving targets are detected by life-detection radar, the targets are lost in the image due to the mutual occlusion of the targets. In this paper, a multi-target imaging method based on multi-station radar is proposed. Two stepped-frequency continuous waves (SFCW) MIMO radars detect targets by forming multiple-stations to obtain multi-directional scattering information of targets. A target image is obtained based on a back-projection (BP) image algorithm, and three image fusion methods of information weighted image fusion, fuzzy logic image fusion and M-N-K detector image fusion are adopted to generate the images of multiple micro-moving targets, including occluded targets. In the experiment, the three image fusion methods are analyzed separately, and the experimental results show that the three methods can effectively recognition the occluded targets.