This work presents a novel image-based vital sign detection strategy for trapped persons based on the optimized 2-D UWB radar deployment. The main novelty is that the slow-time images based on 2-D radar deployment can be created to continuously screen the trapped persons with periodic micro-motions in a specific area. According to the optimized deployment, multiple UWB life-detection mono-static radars are distributed and work in a time-division-multiplexing mode. These radars are sequentially controlled and activated to capture information from all possible wave incidents and scattering angles. The 2-D radar deployment is designed and optimized using the MIMO array topology technique. The vital sign is modeled as the slow-time image, and multiple images might contain the periodic weak responses from the trapped persons. Proof-of-principle experiments were also carried out based on an optimized 2-D UWB radar deployment. All observation points in this deployment are sequentially selected to complete the transmission and reception of the UWB mono-static radar. Finally, the experimental results validate the effectiveness of the proposed image-based vital sign strategy on the azimuth locations of the trapped persons.

Ultra-wideband (UWB) multiple-input multiple-output (MIMO) through-wall radar is widely used in through-wall human target detection for its good penetration characteristics and resolution. However, in actual detection scenarios, weak target masking and adjacent target unresolving will occur in through-wall imaging due to factors such as resolution limitations and differences in human reflectance, which will reduce the probability of target detection. An improved U-Net model is proposed in this paper to improve the detection probability of through-wall targets. In the proposed detection method, a ResNet module and a squeeze-and-excitation (SE) module are integrated in the traditional U-Net model. The ResNet module can reduce the difficulty of feature learning and improve the accuracy of detection. The SE module allows the network to perform feature recalibration and learn to use global information to emphasize useful features selectively and suppress less useful features. The effectiveness of the proposed method is verified via simulations and experiments. Compared with the order statistics constant false alarm rate (OS-CFAR), the fully convolutional networks (FCN) and the traditional U-Net, the proposed method can detect through-wall weak targets and adjacent unresolving targets effectively. The detection precision of the through-wall target is improved, and the missed detection rate is minimized.

In actual life-detection radar applications, a quasi-static person with weak respiration is difficult to find when relying on the echoes from a single fixed observation point. To effectively sense the weak respiration of a quasi-static person in complex through-wall and through-floor conditions, this paper proposes a novel multi-observation point detection system composed of multiple Golay complementary coded radars in which communication and synchronization are carried out wirelessly. The collaboration structure and Golay complementary coded transmitter improve the signal to noise ratio (SNR). Proof-of-principle experiments are carried out with our designed radar prototype and prove that the radar system can detect a respiring target 21 m behind a brick wall or a respiring target behind two levels of reinforced concrete floors, validating the effectiveness of a multi-observation point working mode for the efficient detection of weak human respiration.

In practical situations such as hostage rescue, earthquake and other similar events, the ultra-wideband (UWB) life-detection radar echo response from the respiration motion of the trapped person is always quasi-/non-periodic in respiration frequency or very weak in respiration amplitude, which can be called quasi-static vital sign. Although it is an extremely difficult task, considering the economic cost, the detection ability of the traditional UWB life-detection radars with only a pair of transceiver antennas is desired to be enhanced for locating the quasi-static trapped human being. This article proposes two different detection methods for quasi-static trapped human beings through the single/multiple observation points, which corresponds to the single-/multi-station radar operating mode, respectively. Proof-of-principle experiments were carried out by our designed radar prototypes, validating the effectiveness of the proposed methods.

Based on multiple-input-multiple-output (MIMO) arc array, this contribution resolves the azimuth aliasing limitations of Fourier-based imaging applied to optimized subsampled arrays. In terahertz (THz) azimuth imaging application, we aim to design a Fourier-based anti-aliasing imaging method for sparsely-optimized MIMO arc array to reconstruct the unaliasing THz azimuth image with high azimuth resolution. The implicit array weighting effect of the sparsely-optimized MIMO arc array topology on azimuth point spread function (PSF) reconstruction is analyzed by the performance comparison between the sparsely-optimized MIMO arc array and the normal case. The effective processing flow path with good performances of anti-aliasing and narrow main lobe on imaging reconstruction is proposed, the idea of which is to introduce the operations of zero-padding, aliasing remove and resample. Therefore, based on this sparsely-optimized MIMO arc array topology and the proposed processing flow path, the better azimuth resolution with the limited aperture length and number of transmitters and receivers can be achieved. Finally, a simulation-based THz hair-level target imaging application is presented for validation purpose. Both simulation and experimental results validate the azimuth image capability of 100 μm ~ 200 μm.

The vital signal extraction remains an on‐going challenge due to the low signal‐to‐noise ratio (SNR) of vital signals in real tests. In this Letter, the authors propose a novel vital signal enhancement method using spectral gain instead of the traditional singular value decomposition (SVD) method, which shows excellent performance in the elimination of noise that is in the same frequency band as the vital signal and reducing calculation time. The simulation and experiment have been conducted, and the results indicate that the proposed extraction method outperforms the traditional SVD in output SNR and processing time.