ToA-based multi-target localization and respiration detection using UWB radars
- ChangKyeong Kim^{1} and
- Joon-Yong Lee^{2}Email author
https://doi.org/10.1186/1687-1499-2014-145
© Kim and Lee; licensee Springer. 2014
Received: 15 January 2014
Accepted: 26 August 2014
Published: 8 September 2014
Abstract
This paper proposes a method of detecting the number of persons in an area, along with their locations and breath patterns, using ultra-wideband (UWB) radars. A time-of-arrival type of location estimation was performed in this study not only using techniques introduced in the existing study results of detecting biomedical signals using a UWB radar but also by applying an initial screening method for redundancy reduction and a maximum likelihood observation-target association technique. This paper also introduces radar measurements conducted under a variety of scenarios and presents the results of applying the proposed algorithm to the measured data. The test results showed that the number of targets was accurately estimated with an average positioning accuracy of 12.7 cm.
Keywords
Ultra-wideband radar Multi-target localization Respiration detectionIntroduction
Recently, significant attention has been paid to the non-invasive detection technology of human movement or biomedical signals for the purpose of patient monitoring and search and rescue. An ultra-wideband (UWB) radar is advantageous in terms of being able to sense slow and tiny movement of a human body, as compared to existing Doppler radars [1–3]; therefore, it is regarded as a suitable solution for these application areas. There have been many studies on the technique of detecting not only the human breath but also the heartbeat using a UWB radar [4–9]. It has been found through experimental results that the breath or the heartbeat can be detected not only when there is no obstacle between the radar and the human but even under a situation where the path between the two is blocked by walls [10–15]. Some study results have presented not only single-target detection but also dual-target detection [15–17]. In addition, literature regarding the estimation of the target location as well as distance to a target can be found [15, 18].
To this end, generally, techniques of time-frequency analysis, correlation detection, and static clutter removal have been widely used. Along with them, additional signal processing techniques have been introduced to improve the performance of estimation. In order to remove non-stationary clutter, which is a cause of false alarms, Baboli et al. [19] used a wavelet transform, whereas Zaikov [18] applied a filtering technique. Lazaro et al. [20] and Sharafi et al. [21] showed that biomedical signals can be detected even for a moving target by introducing techniques for movement compensation. The breathing signal generates harmonic components owing to its periodicity [22], which cause false alarms. Lazaro et al. [20] utilized a trap filter to remove them.
This study aims to detect a breathing pattern of one or more persons who breathe at a fixed position and their locations in a two-dimensional space. First, radar scans were obtained in various scenarios in an indoor environment. Most of the radar measurements previously reported in the literature were obtained in scenarios where the front of a person was directed to an antenna. In this study, however, data measured with the side or back of a person directed towards the antenna were also obtained. Although the signals obtained in such scenarios were significantly weak, they could be used successfully for the estimation process. Then, the general detection techniques mentioned above were applied to detect changes in a signal due to a target’s movement. At this step, measurements at each radar may include false alarms that could be caused by target movement regardless of breathing, harmonics, and indirectly reflected signals [17]. Next, an initial screening is conducted to reduce the number of false alarms by analyzing the frequency characteristics of the detected signals. Then, observation-target association is carried out, for which we used a classical maximum likelihood (ML) approach [23]. This approach requires a large number of computations, so a more computationally efficient method must be employed considering practicability. However, this study attempted to show the feasibility and usefulness of the technique, using the distance information and breath frequency information of the target simultaneously in the data association step, using the optimal ML technique. Finally, the number of targets is determined, and the estimates of target locations and breath frequencies according to the determined number is obtained as a final result.
Radar measurements
where the superscript (i) indicates the index for the radar, τ denotes the propagation delay of a reflected waveform (fast time) and contains the distance information of a target, K is the number of multipath signals, and t denotes the measurement time (slow time). In addition, ${a}_{k}^{\left(i\right)}\left(t\right)$ and ${\beta}_{k}^{\left(i\right)}\left(t\right)$ exhibit the scale and time delay of multipath signal components received at the i th radar, respectively. Signal s(τ) is the template signal defined in (1) and n(τ) is the noise. The multipath signal components of a received signal include not only the signal components reflected from the human body but also the signal components reflected from other background objects. Each radar uses its unique pseudo-random code, and thus, the signal transmitted from each radar is assumed to have no interference with the signals received at other radars. When measuring received signals, each radar adopts an average for the transmission of 4,096 pulses, thereby increasing the signal-to-noise ratio of the received signal, and samples were taken every 0.2 s.
Multi-target localization
Detection
where each row vector in the matrix indicates a detected point and it is assumed to satisfy ${r}_{1}^{\left(i\right)}\le {r}_{2}^{\left(i\right)}\le \cdots \le {r}_{{k}_{i}}^{\left(i\right)},\phantom{\rule{1em}{0ex}}\forall i$. The number of observation vectors is denoted by k_{ i }.
Initial screening
- 1.
$\left|{\lambda}_{j}^{\left(i\right)}-{\lambda}_{l}^{\left(i\right)}\right|<{\theta}_{\lambda}$,
- 2.
$\left|\angle {S}_{\mathit{\text{xs}},\text{BP}}^{\left(i\right)}\left({r}_{j};{\lambda}_{j}\right)\pm \angle {S}_{\mathit{\text{xs}},\text{BP}}^{\left(i\right)}\left({r}_{l};{\lambda}_{l}\right)\right|<{\theta}_{p}$,
Data association and parameter estimation
Now, an observation-target association process is conducted, in which observations included in matrices ${\left\{{\stackrel{~}{\mathcal{R}}}^{\left(i\right)}\right\}}_{i=1}^{3}$ are partitioned into the combination of the number of targets. First, a combination that maximizes the likelihood of the observed measurements is searched for, assuming that the number of targets is known as n, and the joint distribution of the measurement errors of the parameters to be estimated is also known. This process is conducted with regard to all possible n values, and during this process, not only the optimal combination but also the optimal values of the location of a target and breath frequency are also found.
where ${a}_{j,n,m}^{\left(i\right)}$ is the measured distance between the i th radar and j th target designated by matrix ${\mathcal{C}}_{n,m}$.
Determination of the number of targets
respectively.
Summary of the estimation algorithm
- 1.
Cross-spectral density ${S}_{\mathit{\text{xs}},\text{BP}}^{\left(i\right)}\left(\mathrm{\Delta \tau};\lambda \right)$ is calculated from signal ${\left\{{r}^{\left(i\right)}\left(\tau ;t\right)\right\}}_{i=1}^{3}$ measured at each reference radar.
- 2.
Distance and breath frequency information of potential targets is detected from ${S}_{\mathit{\text{xs}},\text{BP}}^{\left(i\right)}\left(\mathrm{\Delta \tau};\lambda \right)$. Using the observations obtained at this step, matrix ${\mathcal{R}}^{\left(i\right)}$ is generated.
- 3.
Among the observations detected at each radar, those that have a high probability of being generated by indirect reflections are searched and removed. These can be searched by comparing the frequency information of the observation and phase of the corresponding spectral density.
- 4.
Using the observations left, matrix ${\stackrel{~}{\mathcal{R}}}^{\left(i\right)}$ is generated. Here, if the number of the observations included in ${\stackrel{~}{\mathcal{R}}}^{\left(i\right)}$ is ${\stackrel{~}{k}}_{i}$, the number of potential targets, n, satisfies $1\le n\le N=\underset{1\le i\le 3}{min}{\stackrel{~}{k}}_{i}$.
- 5.
Let n=1.
- (a)
With regard to all the possible combinations that create n groups, each group consists of three observations selected from matrices ${\left\{{\stackrel{~}{\mathcal{R}}}^{\left(i\right)},\right\}}_{i=1}^{3}$, and matrix ${\left\{{\mathcal{C}}_{n,m}\right\}}_{m=1}^{{M}_{n}}$ is generated.
- (b)
The optimal data association index, μ _{ n }, is searched for according to the ML criterion.
- 6.
Increase n by 1. If n≤N, go to step 5a; otherwise, go to step 7.
- 7.
By finding a value of n where a ratio of $\mathcal{\mathcal{L}}\left(n,{\mu}_{n}\right)$ according to n is increased above a specific threshold value, this value is selected as the estimate, ν, of the number of targets.
- 8.
The ML estimates, ${\left\{{\widehat{\mathit{\phi}}}_{j}\right\}}_{j=1}^{\nu}$ and ${\left\{{\widehat{\mathit{\lambda}}}_{j}\right\}}_{j=1}^{\nu}$, of the location and breath frequency of ν targets are determined, respectively.
Test results
Summary of the test results on 10 experiment sets
Measurement | Number | Number of | Number | Estimated number | Location | Estimate of breathing | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
number | of targets | observation vectors | of ambiguities | of targets | error (m) | frequency (Hz) | |||||||||
k _{1} | k _{2} | k _{3} | ${\stackrel{~}{k}}_{1}$ | ${\stackrel{~}{k}}_{2}$ | ${\stackrel{~}{k}}_{3}$ | Target 1 | Target 2 | Target 3 | Target 1 | Target 2 | Target 3 | ||||
1 | 1 | 3 | 6 | 4 | 3 | 2 | 2 | 24 | 1 | 0.1393 | - | - | 0.23 | - | - |
2 | 1 | 4 | 5 | 3 | 2 | 2 | 2 | 12 | 1 | 0.1397 | - | - | 0.21 | - | - |
3 | 1 | 3 | 7 | 1 | 3 | 7 | 1 | 21 | 1 | 0.1709 | - | - | 0.20 | - | - |
4 | 1 | 3 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 0.1239 | - | - | 0.26 | - | - |
5 | 1 | 4 | 5 | 4 | 1 | 1 | 4 | 4 | 1 | 0.0634 | - | - | 0.30 | - | - |
6 | 2 | 5 | 3 | 7 | 3 | 3 | 6 | 1,314 | 2 | 0.0831 | 0.1134 | - | 0.23 | 0.44 | - |
7 | 2 | 6 | 5 | 6 | 6 | 3 | 6 | 17,208 | 2 | 0.2087 | 0.1583 | - | 0.41 | 0.17 | - |
8 | 2 | 3 | 4 | 3 | 3 | 3 | 3 | 171 | 2 | 0.0350 | 0.2501 | - | 0.21 | 0.42 | - |
9 | 2 | 3 | 2 | 4 | 2 | 2 | 3 | 24 | 2 | 0.0184 | 0.0221 | - | 0.24 | 0.24 | - |
10 | 3 | 6 | 11 | 6 | 5 | 11 | 4 | 1,201,420 | 3 | 0.0572 | 0.3174 | 0.1334 | 0.26 | 0.48 | 0.18 |
Conclusions
The present study proposed a detection technique for the location and breathing pattern of an unknown number of people. The algorithm proposed in this study was applied to 10 data sets measured in an indoor environment and exhibited a significantly high level of estimation accuracy. Through the initial screening process via frequency analysis, a considerable number of false alarms occurring at the detection process could be removed. More remarkably, false alarms, which were not removed by the initial screening, were removed effectively at the data association process. The test results of 10 experimental sets introduced in this study show that all of the false alarms were removed completely. It is an interesting finding of this study that not only the distance information but also breath frequency information of the target can be highly useful in data association.
Because we employed a brute-force approach for data association, the number of ambiguities increased combinatorially as the number of targets increased, in particular, under the presence of many false alarms. Moreover, because the experiments introduced in this study were conducted in a well-controlled environment, they are likely to have more false alarms in a complicated environment such as search-and-rescue situations than in our experimental environment. This will create a heavy computational load, so an application with a more efficient data association technique will be required for future work.
Consent
Written informed consent were obtained from the patients for the publication of the accompanying image.
Declarations
Acknowledgements
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0025422). The authors would like to thank Taechong Cho, Dongbok Ki, Bong Ho Cho, and Jihoon Yoon for their assistance in taking the measurements mentioned in this paper.
Authors’ Affiliations
References
- Lubecke VM, Boric-Lubecke O, Awater G, Ong P-W, Gammel P, Yan R-H, Lin JC: Remote sensing of vital signs with telecommunications signals. In World Congress on Medical Physics and Biomedical Engineering (WC2000). Chicago, IL, USA, 23–28 July 2000);Google Scholar
- Droitcour A, Lubecke VM, Lin J, Boric-Lubecke O: A microwave radio for doppler radar sensing of vital signs. In Proceedings of 2001 IEEE MTT-S International Microwave Symposium. Phoenix, AZ, USA, 20–24 May 2001; 175-178.Google Scholar
- Li C, Lin J: Complex signal demodulation and random body movement cancellation techniques for non-contact vital sign detection. In Proceedings of 2008 IEEE MTT-S International Microwave Symposium Digest. Atlanta, GA, USA, 15–20 June 2008; 567-570.Google Scholar
- Staderini EM: UWB radars in medicine. IEEE Aerospace Electron. Syst. Mag 2002, 17(1):13-18. 10.1109/62.978359View ArticleGoogle Scholar
- Chen Y, Gunawan E, Low KS, Kim Y, Soh CB, Leyman AR, Thi LL: Non-invasive respiration rate estimation using ultra-wideband distributed cognitive radar system. In Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2006). New York, NY, USA, 30 Aug–3 Sept 2006; 920-923.Google Scholar
- Immoreev I, Tao T-H: UWB radar for patient monitoring. IEEE Aerospace Electron. Syst. Mag 2008, 23(11):11-18.View ArticleGoogle Scholar
- Soganci H, Gezici S, Arikan O: A bayesian approach to respiration rate estimation via pulse-based ultra-wideband signals. In Proceedings of 2009 IEEE International Conference on Ultra-Wideband (ICUWB 2009). Vancouver, BC, Canada, 09–11 Sept 2009; 9-11.Google Scholar
- Lai JCY, Xu Y, Gunawan E, Chua EC-Maskooki PA, Guan YL, Low K-S, Soh CB, Poh C-L: Wireless sensing of human respiratory parameters by low-power ultrawideband impulse radio radar. IEEE Trans. Instrum. Meas 2011, 60(3):928-938.View ArticleGoogle Scholar
- Salmi J, Molisch AF: Propagation parameter estimation, modeling and measurements for ultrawideband MIMO radar. IEEE Trans. Antennas Propag 2011, 59(11):4257-4267.MathSciNetView ArticleGoogle Scholar
- Chia MYW, Leong SW, Sim CK, Chan KM: Through-wall UWB radar operating within FCC’s mask for sensing heart beat and breathing rate. In Proceedings of 2005 European Radar Conference (EURAD 2005). Paris, France, 03–04 Oct 2005; 267-270.Google Scholar
- Sachs J, Aftanas M, Crabbe S, Drutarovsk M, Klukas R, Kocur D, Nguyen TT, Peyerl P, Rovnakova J, Zaikov E: Detection and tracking of moving or trapped people hidden by obstacles using ultra-wideband pseudo-noise radar. In Proceedings of 2008 European Radar Conference (EuRAD 2008). Amsterdam, Netherlands, 30–31 Oct 2008; 408-411.Google Scholar
- Levitas B, Matuzas J: UWB radar for breath detection. In Proceedings of the 11th International Radar Symposium (IRS 2010). Vilnius, Lithuania, 16–18 July 2010; 1-3.Google Scholar
- Singh S, Liang Q, Chen D, Sheng L: Sense through wall human detection using UWB radar. EURASIP J. Wireless Commun. Netw 2011, 2011: 1-11.View ArticleGoogle Scholar
- Li W, Jing X, Li Z, Wang J: A new algorithm for through wall human respiration monioring using GPR. In Proceedings of the 14th International Conference on Ground Penetrating Radar. Shanghai, China, 04–08 June 2012; 947-952.Google Scholar
- Wang Y, Liu Q, Fathy AE: Simultaneous localization and respiration detection of multiple people using low cost UWB biometric pulse Doppler radar sensor. In 2012 IEEE MTT-S International Microwave Symposium Digest. Quebec, Canada, 17–22 June 2012; 1-3.Google Scholar
- Higashikaturagi K, Nakahata Y, Matsunami I, Kajiwara A: Non-invasive respiration monitoring sensor using UWB-IR. In Proceedings of the 2008 IEEE International Conference on Ultra-Wideband (ICUWB 2008). Hannover, Germany, 10–12 Sept 2008; 101-104.View ArticleGoogle Scholar
- Li J, Liu L, Zeng Z, Liu F: Simulation and signal processing of UWB radar for human detection in complex environment. In Proceedings of the 14th International Conference on Ground Penetrating Radar. Shanghai, China, 04–08 June 2012; 1-3.Google Scholar
- Zaikov E: UWB radar for detection and localization of trapped people. In Proceedings of the 11th International Radar Symposium. Vilnius, Lithuania, 16–18 June 2010; 1-4.Google Scholar
- Baboli M, Boric-Lubecke O, Lubecke V: A new algorithm for detection of heart and respiration rate with UWB signals. In Proceedings of the 34th Annual International Conference of the IEEE EMBS. San Diego, CA, USA, 28 Aug–01 Sept 2012; 3947-3950.Google Scholar
- Lazaro A, Girbau D, Villarino R: Analysis of vital signs monitoring using an IR-UWB radar. Prog. Electromagnetics Res 2010, 100: 265-284.View ArticleGoogle Scholar
- Sharafi A, Baboli M, Eshghi M, Ahmadian A: Respiration-rate estimation of a moving target using impulse-based ultra wideband radars. Australas Phys. Eng. Sci. Med 2012, 35(1):31-39. 10.1007/s13246-011-0112-2View ArticleGoogle Scholar
- Leib M, Menzel W, Schleicher B, Schumacher H: Vital signs monitoring with a UWB radar based on a correlation receiver. In Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP 2010). Barcelona, Spain, 12–16 April 2010; 1-5.Google Scholar
- Morefield CL: Application of0–1integer programming to multitarget tracking problems. IEEE Trans. Automatic Control 1977, 22(3):302-312. 10.1109/TAC.1977.1101500MathSciNetView ArticleMATHGoogle Scholar
- Kim C, Lee J-Y, Cho T, Ki D, Cho BH, Yoon J: Multi-target localization of breathing humans. In Proceedings of the 2013 IEEE International Conference on Ultra-Wideband (ICUWB 2013). Sydney, Australia, 15–18 Sept 2013; 49-54.View ArticleGoogle Scholar
- Venkatesh S, Anderson CR, Rivera NV, Buehrer RM: Implementation and analysis of respiration-rate estimation using impulse-based UWB. In Proceedings of the 2005 Military Communications Conference (MILCOM 2005). Atlantic City, NJ, USA, 17–20 Oct 2005; 3314-3320.Google Scholar
- Rivera NV, Venkatesh S, Anderson C, Buehrer RM: Multi-target estimation of heart and respiration rates using Ultra Wideband sensors. In Proceedings of the 2006 European Signal Processing Conference. Florence, Italy, 04–08 Sept 2006;Google Scholar
- Chunming W, Guoliang D: The study of UWB radar life-detection for searching human subjects. In Proceedings of the 2012 International Conference on Future Electrical Power and Energy System. Sanya, China, 21–22 Feb 2012; 1028-1033.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.