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.

Abstract-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 five humans and five dogs. Our findings 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 human's 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 radar's practical applications.

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 there's 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.

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