What led to the Versa PNT?

Garrett Payne

It is an all-in-one PNT solution that provides positioning, navigation, and very accurate timing. We can take in GNSS signals, as well as the satellite signals, and integrates that with an IMU for a fused solution. I work on the navigation filter and software inside it. So, I’ve been able to get deep into developing and fine tuning the filter inside for an assured and robust navigation solution. I’ve been able to integrate some other new kinds of PNT technology into that as well. So, I’ve been working on projects with integrating odometry for speed and measurements from a vision-based sensor for position fixing. Those are all complementary PNT sources that help the Versa. You always have a good fused solution, even if you’re in a GNSS-degraded/denied environment.

It sounds like a sort of extreme sensor fusion, integrating every possible PNT source.

Correct. GNSS has global coverage, of course, while some positioning sources, such as UWB, are very local.

Can a Versa on a mobile platform transition seamlessly from one to the other?

It’s all very configurable. You can plug-and-play the sensors that you have. Then, you can check the integrity of each measurement source. For example, if you’re in a GNSS-degraded environment, the Versa has some software that can alert you to that and will automatically filter out those measurements, and then navigate based on the other sensors.

With UWB, if there’s nothing local and already mapped out, could you set up some transmitters very quickly, as needed?

Versa PNT. (Image: Safran Federal Systems (formerly Orolia Defense & Security))

Our goal with this project of integrating UWB technology is to identify the exact sensors that we would need. Then it would just be plug-and-play: you would take a Versa unit and plug in a UWB sensor, and it would be able to automatically detect that and talk to other Versa systems that have UWB transceivers. Once we get all the software figured out, it will be simple in GNSS-denied environments for these UWB transceivers to start talking to each other.

If you have units within a building that all have Versa PNTs with UWB, they can see each other’s relative position, but not their absolute position. However, if one of them is located at a known point, such as the entrance or a corner, that would serve as a reference for the other ones to know where they are within the building.

Right. The technology is proven. There are already sensors that do that in warehouses and other large buildings. We want to take that idea and expand it to other GNSS-denied/degraded locations. It would be the same concept: one Versa unit goes on the edge of an area and knows its location, then broadcasts it to other Versa units with UWB technology, enabling them to determine their absolute location as well.

If 50 meters is not enough to get outside the GNSS-denied/degraded area, you might set up a chain or a mash of as many units as needed.

Correct.

What’s your rough timeline to go live?

Currently, we’re evaluating UWB computer technology from different vendors and integrating it in the software portion. We will probably begin performing full field tests in the first quarter of 2024.

Are there any non-defense applications, such as with first responders?
We also provide very accurate beaconing signals that are used for location purposes. So, this is an additional technology that can be used in GNSS-degraded locations — such as deep urban canyons, jungles, or inside buildings — as long as long as you’re within range of the UWB transceiver.

You could accurately survey a point inside a structure ahead of time. Then you could place your UWB transmitter in that surveyed spot and provide the coordinates to other units for use in positioning.

Right, right. If you’re thinking of a very large building in a city, on every floor you could have a beacon in a very accurately surveyed location. So, if you’re in a rush, you can automatically determine your range from different beacons and use that data to determine your position.

How long has Versa PNT been available? Did it evolve from a previous solution you had?

Our company has been founded on timing. We have VersaSync, which provides very accurate timing signals. We’ve extended on that by adding a navigation solution. Many of our customers are using the timing portion of our platforms to generate very accurate frequency reference signals. It also provides an assured navigation solution by fusing GNSS and inertial data.

What markets and applications are you targeting?

Versa PNT. (Image: Safran Federal Systems (formerly Orolia Defense & Security))

We’re providing precise position, timing, and situational awareness for different applications. Our systems can be used for ground, air, and sea-based applications. We specifically at Orolia Defense and Security [now Safran Federal Systems] market towards the U.S. government, defense organizations, and contractors. Our systems have applications beyond defense and security, as they can be used anywhere accurate position and/or timing is needed.

How does the Versa fit into the larger debate about developing complementary PNT capabilities to compensate for the vulnerabilities of GNSS?

It is an expensive, high-end solution that fits a few niches. Every type of sensor that you’re using for PNT has its strengths and weaknesses. That’s why we have a very accurate navigation filter solution that dynamically evaluates the sensor inputs. GNSS is great but not always accurate or available. Other sensors are also not always reliable. That’s why we try to make the unit and the software inside it as customizable and flexible as possible.

Can you give me a couple of use cases?

If a ground vehicle application is entering a GNSS denied/degraded environment, the Versa PNT’s software will detect any kind of GNSS threat. So, it’s going to cut off the GNSS speed and continue to provide a PNP solution based on inputs from the other sensors — such as an IMU, a speedometer, an odometer, or a camera. They’re all providing you different position feeds, so that you can still have an insured position.
The VersaPNT also contains internal oscillators that can provide very accurate timing signals.

An IMU-derived position drifts, of course, so it needs to be periodically re-initialized.

That’s why it’s important to use a navigation filter that’s initialized with a good position from GNSS or other sources, so that you can estimate and dynamically correct the IMU drift using bias terms and offsets.

<p>The post PNT by Other Means: Safran Federal Systems first appeared on GPS World.</p>

The European Space Agency (ESA) has funded Ampyx Power, developer of a next-generation wind energy technology, and Omnisense, developer of locating and tracking solutions, to develop a robust fail-safe navigation system.

The positioning solution will be used for automated take-off and landing of Ampyx Power’s wind-energy aircraft when applied offshore or over rugged terrain. The technology will be enabling as well for other drones in critical applications.

Ampyx Power develops airborne wind energy systems (AWES) using autonomous tethered aircraft as a means for generating electricity on the ground. The launch and land deck is smaller than the wing span of the aircraft. High accuracy, availability and integrity of the relative positioning between aircraft and platform is required during the final horizontal approach to ensure safe landing of the aircraft in the case of GNSS outage.

The funding will cover the integration into the navigation solution of a local positioning system that seeks to provide 10 centimeters of relative positioning accuracy with 100-Hz update rate and an operating range up to 1 kilometers. Ultra-wideband positioning techniques will be used to make this happen.

“The project allows us to integrate a backup local positioning system into the existing high-end navigation solution,” said Michiel Kruijff, head of technology at Ampyx Power. “This novel navigation technology will ensure that our aircraft can overfly the platform with great accuracy, even in case of GNSS failure. This solution is particularly relevant for use cases in rugged terrain or offshore where other affordable means of relative positioning would be too costly or would offer insufficient performance or availability. We seek such a high level of system robustness both for commercial reasons and for safety reasons, in line with our certification approach with the European Aviation Safety Agency (EASA).”

“We are pleased to offer our innovative local positioning system (LPS) to this project,” said Andy Thurman, CEO at Omnisense. “The closely time-synchronized fusion of ultrawideband (UWB) signals exchanged between landing deck and aircraft mounted Omnisense beacons, will allow highly accurate range measurements to be provided to the drone autopilot, enabling continuous operation in the safety critical landing phase. The enhanced capabilities which arise as a result of this project will enable Omnisense to extend the market reach for our flexible LPS offering from the industrial asset and animal tracking markets in which we currently operate, to more dynamic applications such as GNSS denied drone control, autonomous vehicles in smart cities and sports performance analysis.”

<p>The post ESA funds fail-safe navigation system for drones first appeared on GPS World.</p>

Qorvo, a provider of RF solutions, is acquiring Decawave, as well as Custom MMIC. Financial details have not been disclosed.

“This acquisition is by far the biggest in the indoor location industry,” according to Bruce Krulwich, founder of Grizzly Analytics. “While the price is not disclosed, I and others have estimated it at $400-500 million.”

“Apple is using their own UWB chips in upcoming iPhones, but their own chips are too big and use too much power to be used in smartwatches or other small devices,” Krulwich said. “Decawave’s chips will enable Qurvo to sell compatible UWB chips to a much wider range of markets.Apple’s use of UWB in iPhones is the tipping point for UWB. With Apple’s stamp of approval, UWB will be incorporated into a wide range of location-aware electronics, including robots, drones, wearables, smartwatches and more.”

“The biggest implications for this acquisition are not only in the RTLS market, but also in the areas of internet of things, wearables and location-aware electronics,” Krulwich said. “UWB is being used in next-generation products like drones by Intel, robots by iRobot, and autonomous vehicle movement by Segway.”

Bob Bruggeworth, president and chief executive officer of Qorvo, said in a third-quarter financial release that the company was “looking forward to welcoming two industry-leading teams, Decawave and Custom MMIC, to the Qorvo family, expanding our technology portfolio and product offerings.”

Decawave is an Irish fabless semiconductor company specializing in precise location and connectivity applications. The acquisition will advance market penetration of IR-UWB and enable broad global adoption of the technology.

Decawave was founded in Dublin in 2007 by current CEO Ciaran Connell and CTO Michael McLaughlin. The co-founders had a vision that the new IR-UWB technology, based on a nascent IEEE standard, could deliver ultra-accurate location in a way that would revolutionize people’s lives like GPS did in the 1990s.

Twelve years later, IR-UWB is on the verge of becoming the next essential component technology, like GPS, Wi-Fi and Bluetooth before it. Already shipping in millions of smartphones and cars, and across more than 40 other verticals, IR-UWB is enabling accurate indoor location services, secure communications, context aware user interfaces and advanced analytics.

“We are thrilled to announce the acquisition of Decawave by Qorvo,” said co-founder and CEO Ciaran Connell. “We have created an incredibly unique technology, but we understand that to embrace the opportunity in front of us, we will need greater resources to execute at scale, accelerate our innovation and product launches and to continue to support our growing customer base with the same level of service.

“Joining forces with Qorvo’s leading expertise in RF technology, their experience in serving very high-volume markets like Mobile but also the thousands of customers in Industrial and Enterprise, is, for Decawave, a perfect combination to scale and further accelerate the adoption of IR-UWB.”

Eric Creviston, President of Qorvo Mobile Products, said, “We’re very pleased to welcome the Decawave team, which we believe will enhance Qorvo’s product and technology leadership while expanding new opportunities in mobile, automotive and IoT. We look forward to building on the groundbreaking work that Decawave has done and helping to drive new applications and businesses using their unique UWB capability.”

Decawave co-founder Michael McLaughlin added, “From proving a new technology, to building new markets and to today joining a Tier 1 semiconductor company, the past 12 years have been a challenging and fantastic journey.

“None of this would have been possible without the dedication and passion of Decawave employees as well as the constant support from our lead investor Atlantic Bridge, Act Venture Capital, Summit Bridge, Enterprise Ireland and our business angels. To all others who accompanied us on this journey we also say a sincere and profound thank you and we look forward to the next chapter for IR-UWB.”

In the coming months and years Decawave and Qorvo will:

• Continue to contribute to the IEEE, Car Connectivity Consortium, FiRa and UWB alliance to define next-generation PHYs and protocols, ensuring interoperability across applications and fueling IR-UWB adoption,
• Accelerate the roadmap of ICs and modules, leveraging their respective R&D strengths and product portfolio to bring even more IR-UWB solutions to the market,
• Pursue existing partnerships and investments in enablement to offer flexible and easy to integrate IR-UWB solutions to our customers.

<p>The post Big acquisition: Qorvo to acquire location company Decawave first appeared on GPS World.</p>

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

INNOVATION INSIGHTS with Richard Langley

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.

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.

FIGURE 1. UWB antennas setup for the proposed location approach.

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.

FIGURE 2. The proposed system diagram.

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.

FIGURE 3. Proposed scheme for UWB localization in realistic environments with multipath situations.

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.

FIGURE 4. (Top) Layout of test setup. (Bottom left) The acquired imaging on shoulder plane before thresholding. (Bottom right) After thresholding.

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.

FIGURE 5. (a) Setup of anchor station. (b) Pre-defined geometry setup for anchor stations used for the experiment of Figure 4.

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.

FIGURE 6. UWB localization: estimated and actual positions of the antennas placed on the body for the environment as shown in Figure 5.

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.

TABLE 1. Average localization error in centimeters for different testing environments with different estimation methods.

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.

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.

<p>The post Innovation: Indoor positioning using wearable ultra-wideband antennas first appeared on GPS World.</p>

The FXUWB10, UWC.01 and UWCCP.01 ultra-wideband antennas by Taoglas.

Applications include asset tracking, follow-me drones, healthcare monitoring, smart home services and other applications that demand high-performance indoor localization capabilities, the company said.

The antennas offer high efficiencies across a wide spectrum of frequency bands, from 3 GHz to 10 GHz.

Indoor wireless positioning has long been hampered by technologies that were not designed for this purpose, such as Bluetooth, Wi-Fi and assisted GPS.

Taoglas will be exhibiting in Booth 614 at Mobile World Congress Americas, Sept. 12-14, in San Francisco.

Ultra-Wideband. UWB is a low-power digital wireless technology that offers significant increases in location precision and range while transmitting large amounts of digital data short distances over a wide spectrum of frequency bands. UWB’s low-power requirements offer increased battery life of sensors and tags, leading to reduction in overall operational costs.

Taoglas’ range of UWB antennas, designed in Taoglas’ Munich, Germany, engineering center, features both state-of-the-art flexible embedded UWB antennas and UWB embedded SMT chip antennas. According to the company, the flexible FXUWB range of antennas were developed utilizing a “peel and stick” assembly process, attaching securely to non-metal surfaces via 3M adhesive with a flexible micro-coaxial cable mounting.

The UWB chip antennas are designed to be surface mounted directly onto a printed circuit board (PCB). Both series of antennas help designers future-proof devices, keeping costs low while covering all common UWB commercial bands.

“Today’s emerging applications require very precise indoor localization of assets, objects and people,” said Ronan Quinlan, co-CEO for Taoglas. “UWB can work as a type of ‘indoor GPS’ to help solve the precision dilemma for indoor applications, bringing much greater levels of precision than current technologies. We optimize complex antenna performance parameters such as the Group Delay, Polarization and Fidelity Factor. Taoglas’ first-to-market line of UWB antennas are designed to help our customers capitalize on this need for real-time precision localization.”

Autonomous Antenna. One antenna that Taoglas co-developed exclusively with DecaWave is the UWCCP.01 circularly polarized chip antenna, a mass-market antenna specifically designed to enable a new generation of autonomous applications.

The DecaWave DW1000 chip.

The UWB antennas were designed for use with the DecaWave DW1000 chip and are also compatible with any other UWB sensor modules on the market, the company added. Since its launch in December 2013, more than 3.5 million units of the DW1000 have shipped across multiple industries.

From real-time location of people and assets in factories, hospitals and mines, to automotive keyless entry systems, to drones, connected home and sports, the accurate location and secure communications capability of the DW1000 has already taken numerous applications to new heights.

“Antennas play a key role in our customers’ applications. Performance is a given for customers but the capability to adapt to the constraints of the applications — size, shape, electronics environment — is equally important as end products get smaller and smaller,” said Ciaran Connell, CEO and co-founder, DecaWave. “DecaWave is really pleased to partner with Taoglas, as their expertise is not only in delivering high-performance, off-the-shelf antennas, but also to provide customization services that will be highly beneficial to our customers.”

<p>The post Taoglas launches ultra-wideband antennas for indoor positioning first appeared on GPS World.</p>