Experimental study in breath detection and human target ranging in the presence of obstacles using ultra-wideband signals

In this paper, we propose a novel energy detection (ED) receiver architecture combined with time-of-arrival (TOA) estimation algorithm, compliant to the IEEE 802.15.4a standard. The architecture is based on double overlapping integrators and a sliding correlator. It exploits a series of ternary preamble sequences with perfect autocorrelation property. This property ensures coding gain, which allows an accurate estimation of power delay profile (PDP). To improve TOA estimation, the interpolation of PDP samples is proposed and the architecture is validated by using an ultra-wideband signals measurements platform. These measurements are carried out in line-of-sight and non-line-of-sight multipath environments. The experimental results show that the ranging performances obtained by the proposed architecture are higher than those obtained by the conventional architecture based on a single-integrator in both LOS and NLOS environments.

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Usage of integral representations for the surface current density when solving the problem on radiation of ultra wideband signals by unenclosed bent shields

4th International Conference on Antenna Theory and Techniques (Cat. No.03EX699) ◽

10.1109/icatt.2003.1238802 ◽

2004 ◽

Author(s):

V.I. Zamyatin ◽

G.V. Yermakov

Keyword(s):

Current Density ◽

Surface Current ◽

Integral Representations ◽

Ultra Wideband ◽

Surface Current Density ◽

Wideband Signals

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

Sensors ◽

10.3390/s18113873 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3873 ◽

Cited By ~ 3

Author(s):

Hao Lv ◽

Teng Jiao ◽

Yang Zhang ◽

Fulai Liang ◽

Fugui Qi ◽

...

Keyword(s):

Continuous Wave ◽

Doppler Radar ◽

Ultra Wideband ◽

Time Dimension ◽

Processing Scheme ◽

Practical Applications ◽

Uwb Radar ◽

Operational Frequency ◽

Stepped Frequency ◽

Breath Detection

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.

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In-Body Ranging with Ultra-Wideband Signals: Techniques and Modeling of the Ranging Error

Wireless Communications and Mobile Computing ◽

10.1155/2017/4313748 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 6

Author(s):

Muzaffer Kanaan ◽

Memduh Suveren

Keyword(s):

Neural Networks ◽

Human Body ◽

Extreme Value Distribution ◽

Generalized Extreme Value Distribution ◽

Ultra Wideband ◽

Wideband Signals ◽

Ranging Error ◽

And Performance ◽

Ranging Errors ◽

Wireless Capsule

Results about the problem of accurate ranging within the human body using ultra-wideband signals are shown. The ability to accurately measure the range between a sensor implanted in the human body and an external receiver can make a number of new medical applications such as better wireless capsule endoscopy, next-generation microrobotic surgery systems, and targeted drug delivery systems possible. The contributions of this paper are twofold. First, we propose two novel range estimators: one based on an implementation of the so-called CLEAN algorithm for estimating channel profiles and another based on neural networks. Second, we develop models to describe the statistics of the ranging error for both types of estimators. Such models are important for the design and performance analysis of localization systems. It is shown that the ranging error in both cases follows a heavy-tail distribution known as the Generalized Extreme Value distribution. Our results also indicate that the estimator based on neural networks outperforms the CLEAN-based estimator, providing ranging errors better than or equal to 3.23 mm with 90% probability.

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