Tag: ultra-wideband

Body Fitting

UWB is being used in a novel microwave imaging and localization system, one which features Antonio Vivaldi’s namesake antenna.

By Fengzhou Wang and Guohua Wang

VIVALDI. No, you aren’t reading an article in Gramophone. This happens to be the name of a particular kind of broadband antenna, which is particularly useful at microwave frequencies and for ultra-wideband (UWB) applications in particular. It was invented by the British electrical engineer Peter J. Gibson in 1978 while working at Philips Research Laboratories. In a 1979 conference paper entitled “The Vivaldi Aerial,” Gibson described it as “a new member of the class of aperiodic continuously scaled antenna structures and, as such, it has theoretically unlimited instantaneous frequency bandwidth.” He went on to say “This aerial has significant gain and linear polarisation and can be made to conform to a constant gain vs. frequency performance. One such design has been made with approximately 10 dBI gain and -20 dB sidelobe level over an instantaneous frequency bandwidth extending from below 2 GHz to above 40 GHz.” Broadband indeed!

So why did Gibson name the innovative antenna “the Vivaldi aerial”? It has to do with its shape. Another term for the Vivaldi antenna (sometimes called the Vivaldi notch antenna) is the tapered slot antenna. The planar antenna, constructed out of thin metal sheet or printed circuit board (PCB), features a slot line gap cut out of the sheet or etched from the PCB, which gradually flares in the direction of wave propagation (see Figure 1 in this month’s article to see what a Vivaldi antenna actually looks like). Since the spacing of the gap is related to the wavelength of the radio waves that can be launched, the antenna can be used over a wide frequency range not unlike the log-periodic antenna used in shortwave broadcasting or the biconical antenna and its butterfly antenna subtype used for UHF TV reception. Of course, according to the reciprocity theorem, an antenna designed to transmit radio waves can generally be used to receive radio waves with the same antenna properties (gain, bandwidth and so on).

But let’s get back to the tapered slot antenna’s formal name. According to one his co-workers, the shape of the antenna reminded Gibson (who was also a musician and composer) of the cross-section of an early trumpet. So he named his antenna after Antonio Vivaldi, the famous baroque music composer, who wrote several concertos featuring trumpets. And 1978, the year of the antenna’s invention, was the three-hundredth anniversary of Vivaldi’s birth. It doesn’t hurt that the shape of the slot also looks a bit like a cursive “V” when the antenna is stood on its end.

While the basic Vivaldi antenna generates (or receives) linearly polarized waves, it is possible to combine two elements at right angles to generate (or receive) circularly polarized waves.

Because of its broadband characteristics and ease of PCB manufacturing, the Vivaldi antenna has been used extensively in UWB applications. Conventional radio transmissions use a variety of modulation techniques but most involve varying the amplitude, frequency and/or phase of a sinusoidal carrier wave. But in the late 1960s, it was shown that one could generate a signal as a sequence of very short pulses, which results in the signal energy being spread over a large part of the radio spectrum. Initially called pulse radio, the technique has become known as impulse radio ultra-wideband or just ultra-wideband for short. The bandwidths of UWB signals are quite large. For example, in the U.S., current Federal Communications Commission rules for pulse-based positioning or localization implementations require the applied bandwidth to be between 3.1 and 10.6 GHz and the bandwidth to be greater than 500 MHz or the fractional bandwidth to be more than 0.2.

The use of large transmission bandwidths offers a number of benefits, including accurate ranging and that application in particular is being actively developed for positioning and navigation in environments that are challenging to GNSS such as indoors and built-up areas.

In this month’s column, we learn how UWB is being used in a novel microwave imaging and localization system, one which features Antonio Vivaldi’s namesake antenna.

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Indoor localization is challenging work using traditional location-based services such as GPS. Approaches for indoor position estimation have used radio-frequency (RF) signals including narrowband signals such as Wi-Fi and Bluetooth. Impulse radio ultra-wideband (UWB) signals have also been widely investigated. Compared with narrowband signals, UWB signals provide high signal-to-noise ratio, which helps to provide an accurate estimate of signal arrival time for time-based location algorithms such as time of arrival (TOA). Furthermore, UWB signals provide larger coverage areas and a ranging capability. Sub-millimeter positioning accuracy is achievable. And UWB-based location has an inherent high time resolution making it useful in a tracking system for medical and other applications.

A number of investigations in UWB positioning have already been carried out, with several relatively expensive commercial UWB kits available from companies such DecaWave and BeSpoon. But additional work still needs to be carried out to fully evaluate the UWB solution, so this is still an open research topic. One problem area requiring further investigation is positioning in the non-line-of-sight (NLOS) environment. This is considered the main challenge for UWB location, since it is associated with strong fading due to reflection and diffraction from various obstructions such as furniture in the room. Various threshold crossing methods using techniques of energy detection, correlation and the multiple signal classification (MUSIC) spectral analysis algorithm have been used to resolve the multipath propagation problem in NLOS environments. However, these approaches require complicated signal processing, which increases the computing cost.

Moreover, UWB technology is also being widely introduced in microwave imaging for military and biological applications. It provides high-precision detection and high-resolution images, depending, in part, on the operating frequency range. The radar-based microwave imaging or MWI is a time-domain confocal imaging method that aims to indicate the position of the targets by use of the delay time of the reflected signal. MWI technology highlights the target from the testing environment by using different values of the dielectric permittivity constant.

In this article, we propose a hybrid method combining MWI and localization of body-worn UWB antennas for improving the accuracy of indoor positioning. The proposed system will be able to differentiate an LOS environment from an NLOS environment using MWI detection ability, and then adjust the scanning antenna array setup using robotic support. Furthermore, we introduce a threshold value in the filter function to highlight major obstructions in an NLOS environment such as a physical item. Using this proposed system for TOA measurements, we have obtained an overall average accuracy in two-dimensional localization of around 1.7 to 2.5 centimeters.

SYSTEM EXPERIMENTAL SETUP

We have developed a robotic antenna array for indoor microwave imaging to assist in indoor location with wearable antennas. The basic architecture of the proposed UWB localization system consists of two components: tag antennas and anchor antennas. Two thin-film tag antennas are worn on both shoulders of a human, and seven wideband Vivaldi antennas (also known as tapered slot antennas), acting as anchor antennas, are mounted on individual robotic supports, which can adjust the height and the rotation angle of each antenna. All the antennas are fabricated with printed-circuit board (PCB) material to reduce the cost.

In FIGURE 1, the Vivaldi antennas are shown with blue dots and are placed on the top of the robotic support 2 meters above the ground. The antenna array covers a scanning area with a radius of 2 meters. The two compact wearable tag antennas are placed on the left and right shoulders of the target human at a nominal height of 1.7 meters.

Other main components of the proposed system are shown in FIGURE 2.

The system can be manually controlled by an Apple iPad or automatically controlled by a personal computer (PC). The PC runs the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) programming environment and an NI instrument monitor for debugging the operating process. Further information processing is carried out by combining the received signal from a vector network analyzer (VNA) though the USB-based NI-DAQmx driver software and associated cable and a mobile device such as the Apple iPad for remote control and cloud access. Two ports of the VNA are connected to an RF switch to transmit and receive signals using the antennas located in the scanning area. During the detection phase, the anchor antennas are sequentially active, and a number of signal time series are transferred back to the PC for imaging processing. The delay-and-sum algorithm is used for signal processing and imaging reconstruction in Matlab to find the position of any obstruction in the scanning area.

The following specific components were used in the experimental setup shown in Figure 2: an Agilent HP 8510B VNA (operating from DC to 20 GHz for two-channel acquisition), a single-pole eight-throw (SP8T) switch (an Analog Devices HMC321LP4 on an evaluation PCB forming a switchboard), seven directional UWB Vivaldi receiving antennas (operating from 2 to 14 GHz); two body-worn UWB transmitting thin-film antennas (operating from 3 to 9 GHz), a reconfigurable input/output device based on a field-programmable gate array (FPGA) and a microprocessor (NI myRIO-1950 board), a general-purpose interface bus (GPIB) to USB cable (Agilent 82357B), and a personal computer running LabVIEW and Matlab.

PRINCIPLES OF OPERATION

In our proposed technique, the range-based TOA approach is implemented, making use of the high accuracy obtained by the fine time resolution of the applied UWB impulse signal. FIGURE 3 shows a flowchart of the proposed localization scheme in our approach. Initially, the system needs to be calibrated to normalize the responses of all the antennas in the anchor antenna array and to eliminate the effect of reflections from the environment. To calibrate the system for microwave imaging, no objects should be present in the scanning area at this stage.

There are four main phases of the operation. Firstly, the radar-based UWB microwave imaging system is introduced into the localization system to classify the LOS and NLOS environments. If the environment is LOS, the system will go to the location phase directly. If the environment is NLOS, further operations for the antenna array configuration need to be carried out to reduce the multipath effect from the non-target object. In this case, the only located target is the pair of wearable tag antennas.

Secondly, the system moves to the imaging and classification phase involving the Vivaldi antenna array on the anchor station. Using UWB impulses for MWI, the imaging system can detect the existence of inhomogeneity within a structure or medium and a two-dimensional (2D) image can be developed as shown in FIGURE 4.

During the imaging process, one wearable antenna is transmitting a Gaussian pulse while the other is receiving the scattered signals. Circular synthetic aperture radar (CSAR) and elevation-CSAR (E-CSAR) are widely used approaches to extract 2D spatial information of the imaging scenario and have been used for small area 2D remote sensing and foliage target detection. For our current work, we have adopted the CSAR approach. We developed Matlab code to process the data and generate images.

Various material obstructions such as hollow plasterboard boxes, solid concrete items and metal boxes were investigated during our experiments. We had to define threshold values for the various materials to get a more visually acceptable image.

According to the experiments, metal has a significant effect in NLOS environments, and the threshold value was used to optimize the final imaging result (a 20-pixel by 20-pixel image). The scanned area could be visualized with the imaging results depending, in part, on the heights of the antennas on the anchor station and the threshold value used. In this case, two hollow plasterboard boxes are filtered out, leaving the metal box in the image as shown in Figure 4(c).

In the third phase of the operation, the image result is fed into the machine learning algorithm used in the calibration phase. A pre-defined geometry of the antennas on the anchor stations, such as the six anchor stations in a cuboid shape, Y-shape or L-shape, was chosen for implementation in the current environment. The height and angle of the anchor antenna array pattern were adjusted using motors controlled by the NI MyRIO board. In this scenario, all the antennas on the anchor station are receivers (Rxs), and only body-worn antennas are transmitters (Txs).

In this particular experiment, the obstruction (the metal box) is detected on the right upper side of the scanning area, so the cuboid configuration was selected as the anchor station setup. Four antennas on the left of the area were selected as receiving antennas as shown in FIGURE 5. Figure 5(a) highlights one of the antennas.

Finally, in the fourth (location) phase, the time of arrival of the signal from the transmitting antenna array at the receiving wearable antenna is estimated by channel impulse response (CIR) and peak detection techniques. An inverse fast Fourier transform (IFFT) is then applied to obtain the impulse response of the measured channels. The channel impulse response is given by:

where δ is the Dirac delta function, K is the number of resolvable multipath components, τ_k are the delays of the multipath components, a_k are the path amplitude values, and θ_k are the path phase values. The MyRIO board controls the RF switch to circulate each receiving antenna and the corresponding S-parameter value (S₂₁) is passed through the GPIB-to-USB cable for storage in the personal computer. The CIR, a peak detection technique and a TOA data-fusion method are used to accurately estimate the target’s location (x_m, y_m). Let (x_i, y_i) represent the position of the ith transmitting antennas, and r represent the range value obtained from the TOA measurement:

RESULTS

Let us summarize the procedure we followed for an experimental test of our proposed approach as described in the previous section. Our hardware setup is shown in Figure 1, and we carried out the experiment to demonstrate performance in both LOS and NLOS environments. Firstly, a 2D image of the scene area was reconstructed using the time-varying backscattered intensities as shown in Figure 4.

Secondly, the image is processed based on a database to detect the dielectric constants of the obstructions. The shape of the obstruction might not be completely delineated as the low resolution of the image favors an increased efficiency of the imaging processing. However, the position of the obstruction can be found whether it is on a critical path or not. Thirdly, the proper archor-station setup is implemented using the MyRIO board to control the RF switch and antenna motors according to a pre-defined database in the personal computer. Lastly, the peak detection algorithm is used to estimate the TOA of the UWB signal from the Tx at the Rx. The TOA is directly estimated by the detection of the strongest peak of the CIR.

FIGURE 6 shows the localization results for the situation in Figure 4. The same experimental method was repeated but using a threshold-based TOA estimation procedure, and the results compared with our procedure. The results using that approach are also displayed in Figure 6.

In TABLE 1, we summarize the localization errors obtained in the different environments using the two estimation techniques. The average accuracy achieved for our proposed approach for a single antenna is in the range of 3 to 5 centimeters. Given that there are two sensing antennas, one on each shoulder, we could establish a middle point as the position of the human body, and combining the results of each antenna, we could improve the accuracy to about 2.5 centimeters in the NLOS environment.

The method accuracy depends on the pre-defined solution for the anchor antenna array in the NLOS environment, and the estimation accuracy could be improved by training the hardware during the operating period. Furthermore, the localization accuracy also can be enhanced by increasing the number of active anchor stations. However, this will cost more in terms of hardware implementation and also require more space for the apparatus.

CONCLUSIONS

This article presents a hybrid UWB technique combining radar-based microwave imaging and localization of a body-worn UWB antenna for mapping 2D environments. We provided an overview of the concept and method of detecting obstructions, and described a sample implementation that proved the concept and provides ideas for further improvements.

Our results demonstrate the usefulness of the proposed technique, which provides similar performance regarding computational load and accuracy compared to traditional methods using a threshold-energy-based algorithm such as the search-back method and least-edge detection methods. The technique also is able to distinguish between LOS and NLOS environments.

Our approach has some advantages compared to the common methods for NLOS location. One advantage is the reuse of the anchor station for the microwave imaging setup to get low-resolution results for calibration. In addition, the reconfigurable anchor-station setup could be suitable in any NLOS environment with the predefined database. The database could also be improved even after the hardware system is set up. Furthermore, since the radar-based UWB microwave imaging technique uses a short pulse of low-power microwaves in the frequency range 3 GHz to 10 GHz, the measured scattered signal in the far-field can be used for imaging specific material according to its dielectric constant.

However, since the power level of the signal is limited, in part due to safety regulations, it is only detected over a short distance. The UWB pulse has a large bandwidth and, as such, the reflected signals contain a significant amount of information about the target for further imaging applications. Moreover, the anchor-station configuration model can be scaled by a factor suitable for the dimensions of any room or area under observation for a variety of indoor location applications.

A couple of important points to note is that although it is a radio technique, UWB is license-free because of its low power, and UWB technology’s carrier-less transmission property offers the advantage of simple and compact hardware.

Importantly, the performance of our proposed technique achieves more accurate localization of humans, for example, by using two body-worn transmitting antennas, one on each shoulder. The reconfigurable hardware structure under computer control provides the potential for a self-upgrading platform for indoor positioning with a more appropriate anchor-station setup being achieved using machine learning technology.

ACKNOWLEDGMENTS

The authors thank Iain Gold of the School of Engineering, University of Edinburgh, for his help in the fabrication and measurements of the antennas. The authors also acknowledge the Scottish Microelectronics Centre at the University of Edinburgh for measurement equipment support. This article is based on the paper “Localisation of Wearable Ultra-wideband Antenna for Indoor Positioning Application” presented at ION GNSS+ 2017, the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 25–29, 2017.

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FENGZHOU WANG received a B.S. (Hons.) degree in electrical engineering from Birmingham City University in England, and an M.S. degree from the University of Southampton, England. He is working towards a Ph.D. degree in the School of Engineering, University of Edinburgh, Scotland. His research addresses the area of RF sensor systems design and integration.

GUOHUA WANG received his B.S. degree in machinery design and manufacture from Southwest Agricultural University, Chongqing, China; an M.S. degree in agricultural mechanization engineering from China Agricultural University, Beijing, China; and a Ph.D. degree in measurement technology and instrumentation from Beihang University, Beijing, China. He is a lecturer in the School of Instrumentation and Opto-Electronic Engineering in Beihang University. His research interests include automatic testing and partially reconfigurable systems.

FURTHER READING

• Indoor Positioning in General

“Getting Closer to Everywhere: Accurately Tracking Smartphones Indoors” by R. Faragher and R. Harle in GPS World, Vol. 24, No. 10, October 2013, pp. 43–49.

“Recent Advances in Wireless Indoor Localisation Techniques and System” by Z. Farid, R. Nordin and M. Ismail in Journal of Computer Networks and Communications, Vol. 2013, 2013, 12 pp., doi: 10.1155/2013/185138.

“Hybrid Positioning with Smartphones” by J. Liu in Ubiquitous Positioning and Mobile Location-Based Services in Smart Phones, edited by R. Chen, published by IGI Global, Hershey, Pennsylvania, 2012, pp. 159–194.

Ubiquitous Positioning by R. Mannings, published by Artech House, Norwood, Massachusetts, 2008.

“Non-GPS Navigation for Security Personnel and First Responders” by L. Ojeda and J. Borenstein in Journal of Navigation, Vol. 60, No. 3, September 2007, pp. 391–407, doi: 10.1017/S0373463307004286.

• Ultra-Wideband Positioning

“Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis” by A.R.J. Ruiz and F.S. Granja in IEEE Transactions on Instrumentation and Measurement, Vol. 66, No. 8, pp. 2106–2117, August 2017, doi: 10.1109/TIM.2017.2681398.

“Ultra-wideband Indoor Positioning Technologies: Analysis and Recent Advances” by A. Alarifi, A. Al-Salman, M. Alsaleh, A. Alnafessah, S. Al-Hadhrami, M.A. Al-Ammar and H.S. Al-Khalifa in Sensors, Vol. 16, No. 5, 707, 36 pp., 2016, doi: 10.3390/s16050707.

“Where Are We? Positioning in Challenging Environments Using Ultra-Wideband Sensor Networks” by Z. Koppanyi, C.K. Toth and D.A. Grejner-Brzezinska in GPS World, Vol. 26, No. 3, March 2015, pp. 44–49.

Ultra-wideband Positioning Systems: Theoretical Limits, Ranging Algorithms, and Protocols by Z. Sahinoglu, S. Gezici and I. Guvenc, published by Cambridge University Press, Cambridge, U.K., 2008.

• Time of Arrival Estimation

“Entropy-based TOA Estimation and SVM-based Ranging Error Mitigation in UWB Ranging Systems” by Z. Yin, K. Cui, Z. Wu and L. Yin in Sensors, Vol. 15, No. 5, May 2015, pp. 11701–11724, doi: 10.3390/s150511701.

“Prior Models for Indoor Super-resolution Time of Arrival Estimation” by D. Humphrey and M. Hedley in Proceedings of VTC Spring 2009, the 69th Vehicular Technology Conference, Barcelona, Spain, April 26–29, 2009, 5 pp., doi: 10.1109/VETECS.2009.5073817.

“Ranging with Ultrawide Bandwidth Signals in Multipath Environments” by D. Dardari, A. Conti, U. Ferner, A. Giorgetti and M.Z. Win in Proceedings of the IEEE, Vol. 97, No. 2, February 2009, pp. 404–426, doi: 10.1109/JPROC.2008.2008846.

“A New Time of Arrival Estimation Method Using UWB Dual Pulse Signals” by R. Zhang and X. Dong in IEEE Transactions on Wireless Communications, Vol. 7, No. 6, June 2008, pp. 2057–2062, doi: 10.1109/TWC.2008.070112.

“Threshold-based TOA Estimation for Impulse Radio UWB Systems” by I. Guvenc and Z. Sahinoglu in Proceedings of ICU 2005, IEEE International Conference on Ultra-Wideband, Zurich, Switzerland, Sept. 5–8, 2005, pp. 420-425, doi: 10.1109/ICU.2005.1570024

• Ultra-Wideband Antennas

“Microwave Imaging Using CMOS Integrated Circuits with Rotating 4 × 4 Antenna Array on a Breast Phantom” by H. Song, A. Azhari, X. Xiao, E. Suematsu, H. Watanabe and T. Kikkawa in International Journal of Antennas and Propagation, Vol. 2017, 2017, 13 pp., doi: 10.1155/2017/6757048.

Ultrawideband Antennas for Microwave Imaging Systems by T.A. Denidni and G. Augustin, published by Artech House, Norwood, Massachusetts, 2014.

“The Vivaldi Aerial” by P.J. Gibson in Proceedings of the 9th European Microwave Conference, Brighton, U.K., Sept. 17–20, 1979, pp. 101–105, doi: 10.1109/EUMA.1979.332681.

• Characteristics of Antennas and Their Interaction with Humans

“GNSS Antennas and Humans: A Study of Their Interactions” by J.B. Bancroft, V. Renaudin, A. Morrison and G. Lachapelle in GPS World, Vol. 23, No. 2, February 2012, pp. 60–66.