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Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

In this article, a channel micro-Doppler ($\mu \text{D}$) signatures’ quantitative evaluation method for multiple-input–multiple-output (MIMO) radar system, named relative signal–noise ratio in the time–frequency domain and denoted as RSNRtf, is introduced. The impact on RSNRtf by some key dominant factors in real practical and diverse detection environments was theoretically analyzed first. Then, this proposed method is exploited to conduct effective and superior channel selection for improved human activity classification at random human target positions. Based on numerous MIMO stepped-frequency continuous wave (SFCW) radar data from eight subjects with seven activities at nine positions in a through-wall within a view angle of ±20° and up to 6-m distance away, the influence of various primary factors on the proposed method is investigated. Investigative experiments showed that the proposed method has a wide range of applicability and a high interference tolerance even for different detection distances, angles, activities, and subjects. Classification experimental results based on typical classifiers also showed that the proposed method could effectively improve human activity classification. Moreover, we can claim that the proposed method can achieve a higher outstanding performance in the channel $\mu \text{D}$ capacity evaluation and enhanced classification for the MIMO-radar-based human activity classification in free space or with a wider detection field with large angle range. [ABSTRACT FROM AUTHOR]

Radar-based assisted living has received significant amount of research interest in recent years. However, most of the existing works are focused only on free space detection. When it comes to the through-the-wall human motion detection, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human targets behind wall. In this work, we first propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of human motions detection behind a single layer of reinforced concrete wall are carried out to investigate the performance of the technique. Lastly, a two-dimensional (2D) PCA based method and a convolutional neural network (CNN) based method are employed for the motion classification. The results based on the enhanced features obtained by the proposed method show a higher accuracy than those based on the traditional features through mean value subtraction. [ABSTRACT FROM AUTHOR]

A novel quantitative method to evaluate channel micro-Doppler capacity of multiple-input and multiple-output (MIMO) system is proposed here. The method is valid for ultrawideband (UWB) MIMO radar human activity systems based on time–frequency signatures, and the quality measure will be noted as a relative signal to noise ratio (RSNR). The method quantifies these signatures and evaluates the relative superiority or inferiority of these MIMO channels. Examples of micro-Doppler signature ($\mu $ Ds) characteristics of human activities in a channel will be considered and compared to that of all other channels. First, the MIMO UWB radar human activity signal is modeled, and its corresponding time–frequency ($T$ – $F$) characteristics are analyzed to justify the rationality of using the new RSNR metric. Second, the method is evaluated using experimental data and the capability of distinguishing the $\mu \text{D}$ capacity differences among channels is demonstrated. This new method clearly and accurately shows much better visible $\mu \text{D}$ evaluation performance than that of the conventional signal to noise ratio in time domain (${\mathrm {SNR}}_{t}$). Moreover, this evaluation method can still work well, even for signals with low signal to noise ratio (SNR) down to −4-dB level. Therefore, it can be successfully used to select the superior channels and eliminate any inferior channels or provide confidence coefficients for the collected multiple channel data of human activities. This method should lead to a significant reduction of the inferior channels’ influence on further MIMO-based classification or imaging of human activities. [ABSTRACT FROM AUTHOR]

Noncontact penetrating detection and classification of human activities based on micro-Doppler signatures (MDs) using ultrawideband (UWB) bio-radars are valuable tasks in various practical applications such as post-disaster search-and-rescue operations and urban military operations. However, for all classifiers, MD features of different-magnitude activities at different positions are likely to result in classification errors due to MD attenuation and confusions. This letter proposes a classifier improving method called position-information-indexed classifier (PIIC). It aims at enhancing the performance of various classifiers in terms of recognition and classification. This method fully exploits the position information acquired by UWB bio-radar to create a position-labeled modularized database of MD features. It also guides searching adaptively for optimal predict submodel of PIICs for activity classification at a random position. We report through-wall detection and classification experimental results related to five activities within a range of 6 m. These results, based on four typical classifiers, demonstrate that PIIC-based classifiers can avoid those classification errors in an effective manner. Moreover, all PIIC-based classifiers present a better classification performance with an average accuracy rise of 8.16% compared with those of overall-model-based classifiers. These performance evaluation experiments suggest that this method is strongly robust and stable, presenting wide applicability to various classifiers. [ABSTRACT FROM AUTHOR]

Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing. [ABSTRACT FROM PUBLISHER]

Life detection radar can detect human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance away by penetrating nonmetal mediums (brick walls, ruins, etc.). However, interference is often caused by respiratory movements of the radar's operator when detecting vital signs of another human target. The detection accuracy can be significantly influenced by this kind of interference. In this letter, an experimental setup with a dual-frequency continuous-wave life detection radar is investigated. The system operates with different frequencies of 5.75 and 35 GHz. An adaptive filtering method is applied to suppress the interference caused by the operator's respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy. [ABSTRACT FROM PUBLISHER]

Detection of trapped survivors under collapsed buildings using impulse-radio ultra-wideband (IR-UWB) radar has become an important research topic. However, due to the jitter or drift of the radar, clutter reflected from walls or rubble is similar to the human respiratory response in some scenarios. Thus, it becomes difficult to determine the presence or absence of the survivor under ruins. In this paper, a novel dual-frequency IR-UWB radar with low center frequencies is developed and a method based on adaptive clutter cancellation is proposed to eliminate the respiration-like clutter. Experiments are carried out in both through-wall and simulated rubble cases, and the results shows that the proposed method outperforms the common method, which is based on linear-trend subtraction with single-frequency IR-UWB radar in removing the respiration-like clutter and improves the radar performance in life detection. [ABSTRACT FROM PUBLISHER]