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
Introduction
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
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