Next Article in Journal
Development of a Protein Microarray Chip with Enhanced Fluorescence for Identification of Semen and Vaginal Fluid
Previous Article in Journal
3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3873;

A Novel Method for Breath Detection via Stepped-Frequency Continuous Wave Ultra-Wideband (SFCW UWB) Radars Based on Operational Bandwidth Segmentation

Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, China
These authors contributed equally to this work and should be regarded as co-first authors.
Author to whom correspondence should be addressed.
Received: 11 October 2018 / Revised: 5 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [7595 KB, uploaded 10 November 2018]   |  


Human being detection via ultra-wideband (UWB) radars has shown great prospects in many areas, such as biomedicine, military operation, public security, emergency rescue, and so on. When a person stays stationary, the main feature that separates him/her from surroundings is the movement of chest wall due to breath. There have been many algorithms developed for breath detection while using UWB radars. However, those algorithms were almost based on a basic scheme that focused on processing in the time dimension of UWB data. They did not utilize the benefits from the wide operational bandwidth of UWB radars to show potential superiority over those narrowband systems such as a continuous wave (CW) Doppler radar. In this paper, a breath detection method was proposed based on operational bandwidth segmentation. A basic theoretical model was firstly introduced, indicating that characteristics of breath signals contained in UWB echoes were consistent among the operational frequencies, while those of clutters were not. So, the method divided a set of UWB echo data into a number of subsets, each of which corresponded to a sub-band within the operational bandwidth of the UWB radar. Thus information about the operational frequency is provided for subsequent processing. With the aid of the information, a breath enhancement algorithm was developed mainly by averaging the segmented UWB data along the operational frequency. The algorithm’s performance was verified by data measured by a stepped-frequency CW (SFCW) UWB radar. The experimental results showed that the algorithm performed better than that without the segmentation. They also showed its feasibility for fast detection of breath based on a short duration of data. Moreover, the method’s potential for target identification and impulse-radio (IR) UWB radar was investigated. In summary, the method provides a new processing scheme for UWB radars when they are used for breath detection. With this scheme, the UWB radars have a benefit of greater flexibility in data processing over those narrowband radars, and thus will perform more effectively and efficiently in practical applications. View Full-Text
Keywords: UWB radar; human being; breath detection; operational bandwidth; segmentation UWB radar; human being; breath detection; operational bandwidth; segmentation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Lv, H.; Jiao, T.; Zhang, Y.; Liang, F.; Qi, F.; Wang, J. A Novel Method for Breath Detection via Stepped-Frequency Continuous Wave Ultra-Wideband (SFCW UWB) Radars Based on Operational Bandwidth Segmentation. Sensors 2018, 18, 3873.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top