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

Hao Lv,Fulai Liang,Zhao Li,Yang Zhang,Teng Jiao,Jianqi Wang,Fugui Qi

doi:10.3390/rs8090773

Abstract

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.

Highlights

In recent years, remote sensing of human targets with ultra-wideband (UWB) radar has attracted increasing attention [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
Since the thorax motion is in order of millimeters, and severe attenuation may exist on the propagation path of the electromagnetic waves, detection of human respiration using UWB radar is a challenging task
Inspired by the multi-channel and multi-transceiving techniques in [11,12,13,14,15,16,17,19,20], this paper proposed a method to solve the body orientation issue based on a multistatic UWB radar

Summary

Introduction

Remote sensing of human targets with ultra-wideband (UWB) radar has attracted increasing attention [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. Since a human target’s body orientation cannot always be guaranteed to be facing toward the radar, the respiratory-motion response contained in UWB echoes is much weaker than expected. This will result in performance degradation of UWB radar when being applied in practice, especially for the applications of non-line-of-sight (NLOS) detection of human targets, such as through-wall surveillance or trapped victim search and rescue after an earthquake. The two studies aimed at applying UWB radar in medicine, or, to be more specific, in sleep apnea monitoring Their experiments were carried out in free space, in which detection of human respiration using UWB radar is relatively easy. For trapped victim detection in post-earthquake emergency rescue, the body orientation problem of UWB radar has been referred to, but not provided any solution [10]

Methods

Results

Discussion

Conclusion