Teng Jiao

An Interference Suppression Technique for Life Detection Using 5.75- and 35-GHz Dual-Frequency Continuous-Wave Radar

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 radars 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 operators respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy.

Multi-target human sensing via UWB bio-radar based on multiple antennas

Being capable of sensing human through obstacles, bio-radar is promising in many applications like healthcare, public securities, emergency rescue and so on. In these applications, the presence of human and the human count are among the most important issues that are concerned by people. At present plenty of studies deal with the former issue but theres no study dealing with the latter one. To this end, a framework of determining the count of human targets using ultra-wideband (UWB) bio-radar was presented in this paper. It was developed based on multiple antennas and correlation processing of sensed respiration among the data channels. In the experiment, the UWB bio-radar could distinguish among the cases of no target, single target, two targets and three targets present behind a brick wall and determine the target count with no priori information. On this basis, multi-target estimation and localization can be further realized.

Characterization and Identification of IR-UWB Respiratory-Motion Response of Trapped Victims

Impulse-radio ultrawideband (IR-UWB) radar is a popular research topic in the field of post-earthquake search and rescue. By lowering center frequency, it can penetrate through earthquake rubble to detect trapped victims mainly by identifying their respiratory-motion response. Thus, low-center-frequency IR-UWB respiratory-motion response is characterized for the first time in this paper. On this basis, a novel constant false alarm rate (CFAR) algorithm that automatically identifies the response is developed. The IR-UWB respiratory-motion response has range extension and interrelation characteristics. With these characteristics, the algorithm can effectively improve the estimation accuracy of clutter energy in CFAR detection. The characteristics and the algorithm performance are verified by experiment results, which show not only great promise in practice but also significance for future research of IR-UWB radar for detection of trapped victims.

USING WAVELET ENTROPY TO DISTINGUISH BETWEEN HUMANS AND DOGS DETECTED BY UWB RADAR

When using ultra-wide band (UWB) radar to detect targets in various conditions, identifying whether the target buried under building debris or in bad visibility conditions is a human or an animal is crucial. This paper presents the application of the wavelet entropy (WE) method to distinguish between humans and animal targets through brick wall and in free space at a certain distance. In the study, WE, WE change, and WE of the related range points were estimated for the echo signals from flve humans and flve dogs. Our flndings indicate that the entropy or degree of disorder in the energy distribution of the human target was much lower than that of the dog, and the waveform of the humans entropy was smoother than that of the dog. In addition, the body micro motions of humans are much more ordered than those of dogs. WE can be employed as a quantitative measure for recognizing invisible targets and may be a useful tool in the UWB radars practical applications.

Improved Detection of Human Respiration Using Data Fusion Basedon a Multistatic UWB Radar

This paper investigated the feasibility for improved detection of human respiration using data fusion based on a multistatic ultra-wideband (UWB) radar. UWB-radar-based respiration detection is an emerging technology that has great promise in practice. It can be applied to remotely sense the presence of a human target for through-wall surveillance, post-earthquake search and rescue, etc. In these applications, a human target’s position and posture are not known a priori. Uncertainty of the two factors results in a body orientation issue of UWB radar, namely the human target’s thorax is not always facing the radar. Thus, the radial component of the thorax motion due to respiration decreases and the respiratory motion response contained in UWB radar echoes is too weak to be detected. To cope with the issue, this paper used multisensory information provided by the multistatic UWB radar, which took the form of impulse radios and comprised one transmitting and four separated receiving antennas. An adaptive Kalman filtering algorithm was then designed to fuse the UWB echo data from all the receiving channels to detect the respiratory-motion response contained in those data. In the experiment, a volunteer’s respiration was correctly detected when he curled upon a camp bed behind a brick wall. Under the same scenario, the volunteer’s respiration was detected based on the radar’s single transmitting-receiving channels without data fusion using conventional algorithm, such as adaptive line enhancer and single-channel Kalman filtering. Moreover, performance of the data fusion algorithm was experimentally investigated with different channel combinations and antenna deployments. The experimental results show that the body orientation issue for human respiration detection via UWB radar can be dealt well with the multistatic UWB radar and the Kalman-filter-based data fusion, which can be applied to improve performance of UWB radar in real applications.

Detecting and monitoring the micro-motions of trapped people hidden by obstacles based on wavelet entropy with low centre-frequency UWB radar

Efficient detection and monitoring of physiological conditions and activity hidden by walls or rubble are considered challenging in rescue, surveillance, and security operations. In this paper, a new method based on wavelet analysis called wavelet entropy (WE) is proposed to not only detect vital signs but also to monitor the target’s physiological conditions through ultra-wideband (UWB) radar with a centre frequency of 400 MHz. Results of the experiments show that this new method can accurately locate a human target in free space, through a brick wall at a certain distance, and in cases of simulated rubble. Furthermore, a human target’s micro-motion responses due to different physiological conditions with a stationary body in the case of simulated rubble, and that are unrecognizable using common methods, were also quantitatively detected. The WE method not only locates the target’s position but also monitors changes in his/her physiological conditions. This method may also be a useful tool in the practical application of UWB radar.

An Adaptive-MSSA-Based Algorithm for Detection of Trapped Victims Using UWB Radar

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.

The Separation of the Heartbeat and Respiratory Signal of a Doppler Radar Based on the LMS Adaptive Harmonic Cancellation Algorithm

This paper proposed a LMS adaptive harmonic cancellation algorithm, in order to effectively separate the breathing and heartbeat signal from biological radar, so that the physiological characteristic parameter (respiration rate and heart rate) can be real-time monitored. The harmonic combination of the respiratory signal is regarded as reference input of the model, and the bio-radar body motion signal is regarded as the original input of the model. The results suggest that the breathing and heartbeat signal can be well separated by the proposed algorithm. Also, the proposed method is simple and easy to implement. Therefore, it is expect that the proposed method can be applied to separate the breathing and heartbeat signal from Doppler radar echo signal.

Simulation research of microwave automatic clutter-cancellation in life-detection radar

Clutter signal can bring interference to life-detection radar and reduce accuracy of recognition. This research attempts to resolve the problem in clutter jamming by simulation experiment. The automatic clutter-cancellation model is established according to theories and experiments are simulated on computer. The result suggests that the variation of auto-cancellation signal is not correlative with time and precision of the cancellation but amplitude and phase of the clutter signal. The result has instructive significance for the design of digital auto-cancellation circuit in life-detection radar.

Simulation of Single-target Three-dimensional Localization Using Multi-channel Antenna Arrays

Three multi-channel ultra wideband (UWB) radar structures that can detect and locate single human target is proposed. The target three-dimensional localization mathematical models of different antenna arrays are constructed. The feasibility and the accuracy of these systems are verified by computer simulation. The impact factors, such as the distance between transmit and receive antenna, the number of data’s effective digits, and so on, are discussed. The results and conclusion in this paper can serve as reference for the research and development of target threedimensional localization life detection radar in the future.