by James T. Curran, Mark Petovello, and Gérard Lachapelle
Within a day of their initial activation over central Europe on March 12, Galileo satellites were visible over North America. The PLAN Group of the University of Calgary was successful in capturing and processing the signals from these satellites as they emerged. Galileo PRN 11, 12, and 19 were found and tracked on E1B/C. The PLAN software GSNRx was also able to track simultaneously GPS L1 and GLONASS L1 and produce combined position solutions.
Examining the Galileo navigation message transmitted on the E1B signal, it was found that the satellite health status is flagged as E1BHS=3 meaning Signal Component currently in Test, and the data validity status is flagged as E1BDVS=1 meaning Working without Guarantee. Current Galileo-ready commercial receivers may automatically discard measurements from a satellites broadcasting such messages. Parsing the received words in the I/NAV message, it was noted that more 50 percent of them were of type 0, although all words (types 0 to 10) were decoded at some point during the test.
Data was collected using a roof-mounted NovAtel 702GG antenna and an in-house two-channel digitizing front-end clocked by a high quality OCXO and also a three-channel National Instruments front-end for post-processing. The two-channel intermediate frequency data was streamed live to a laptop computer for real-time processing with GSNRx. Two RF channels were processed, the first centered at 1574.0 MHz with an IF bandwidth of 10.0 MHz, for the GPS L1 C/A and Galileo E1B/C signals and the second centered at 1602.0 MHz again with a bandwidth of 10.0 MHz, for the GLONASS L1 OF signals. The GPS and GLONASS signals were tracked using a Kalman-filter-based tracking strategy while the Galileo signals were tracked using a specialized data-pilot algorithm.
Figure 1. Scatter plot of the north and east position
Pseudorange and Doppler observations were extracted from the tracking strategies at a rate of 2 Hz. A 2D horizontal plot of the combined GPS & GLONASS and the combined Galileo, GLONASS & GPS single-frequency single-point solutions is presented in Figure 1.
Figure 2: Skyplot of the Galileo satellites.
The pseudorange residuals are plotted against time for each PRN tracked from each of the three systems in Figure 3. It is apparent that the addition of the three Galileo observations contributes to a reduction in bias and standard deviation in the horizontal directions, showing an excellent functioning of the Galileo satellites and PLAN Group equipment and software.
Figure 3. Pseudorange residuals are plotted against time for each PRN tracked from each of the three systems.Figure 4. A screenshot of the receiver processing the data.
By Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle
INNOVATION INSIGHTS by Richard Langley
GPS IS VIRTUALLY UBIQUITOUS with more than 400 million units estimated to be in use in the United States alone. Some of these units are standalone devices such as those used in surveying and timing applications and those used for vehicle navigation or tracking with permanent or temporary mountings. However, the majority of the units are integrated into cellular telephones, tablet computers, personal digital assistants, watches, cameras, and other devices, which are designed to be operated in close contact with the human body. We even now have GPS shoes!
It is well known that the performance of the antenna of a radio receiver can be affected when it is used in close proximity to the human body. We only have to touch the whip antenna of a portable AM/FM or scanner radio to convince ourselves of the effect. So, when we use a handheld GPS receiver or wear a GPS watch, or put a GPS-equipped cellular telephone up to our ear, are there any effects on the operation of the receiver?
It turns out that there are four major effects that can change the performance of a GPS (or other GNSS) receiver antenna when placed near or on the human body. The impedance of the antenna may be changed causing a drop in power transfer to the receiver front end. The center frequency and bandwidth of the antenna may be changed again resulting in a loss of received power. The gain pattern of the antenna may be changed. However, the change may be favorable, improving reception for a given satellite azimuth and elevation angle. And lastly, there will be close-range multipath between the antenna and the body skin.
All of these factors need to be taken into consideration when a manufacturer is designing a GPS unit to be operated in close proximity to a human body. Trade-offs might be possible and certain designs may make the antenna less likely to interact with its surroundings.
But how does one go about assessing the antenna’s performance in a repeatable and quantifiable way?
In this month’s column, a team of researchers from The University of Calgary report on tests conducted on two different types of GPS antennas operated in the vicinity of a human phantom — an artificial body with similar electromagnetic properties as that of a real human.
“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him with topic ideas, email him at lang @ unb.ca.
GNSS-based navigation is the foundation of many pedestrian navigation systems. The use and benefit of GNSS receivers to locate people has increased dramatically over the past few years. Pedestrian navigation applications include mobile phone users, first responders, health and activity monitoring, consensual tracking (such as offender management), recreational use, and tracking of military personnel. GNSS navigation systems are commonly available in watches and personal entertainment devices. Some applications contain GNSS receivers and antennas in shoes, glasses, and jackets. Since each application using a GNSS receiver to locate people requires an antenna, the optimal type, size, and location on the body is becoming increasingly important.
This article addresses adverse antenna effects when the antenna is placed near or on the human body, specifically in the reactive near field at the GPS L1 frequency. Using real data collected on a human phantom over prolonged periods, the changes within the antenna are observed as a function of distance from the body. Thus, a performance profile can be generated to quantify the power loss incurred by loading the antenna. The study applies equally well to all GNSS operating at or near the GPS L1 frequency.
The researchers have theoretically addressed performance of GPS antennas in close proximity to a human body. Using simulations to provide analysis of antenna detuning effects, one research group showed a 24.4-MHz shift in the resonance frequency of the antenna when placed 10–40 millimeters from a simulated human chest. The resonance shift was common at all distances, although the return loss decreased as the antenna was moved further away from the chest.
A few studies have developed antennas to be located in protective (or otherwise) garments for specific applications. Our team previously analyzed the impact of antenna location on the human body by comparing the solution of eight identical and simultaneous navigation solutions.
Antenna-Body Interaction
Antenna detuning refers to the consequence of the electrical interaction between an antenna and an adjacent object, the body of a user in this context, which causes the center frequency of the antenna to deviate from the desired center frequency. More generally, there are several effects that serve to degrade antenna performance that arise when an antenna operates near the body of a user.
The first of these effects is a change in the impedance of the antenna, as shown in FIGURE 1. (See online sidebar for antenna and electromagnetic radiation term definitions.) The change results in the impedance of the antenna no longer properly matching that of the network that it is expected to drive, therefore causing incomplete power transfer between the antenna element and the subsequent radio-frequency (RF) stages.
Selected Antenna and Electromagnetic Radiation Terms
Axial ratio. A measure of the polarization ellipticity of an antenna designed to receive circularly polarized signals. An axial ratio of unity, or 0 dB, implies a perfectly circularly polarized antenna.
Bandwidth. The range of frequencies over which an antenna is designed to operate efficiently. The bandwidth limits are typically determined by a particular reduction in gain compared to that at the antenna’s center frequency; for example, 3 dB or 10 dB.
Conductivity. A measure of a material’s ability to conduct an electric current. The reciprocal of resistivity. Units are mhos per meter.
Dielectric. A material in which there are no free charges that can move through it under the influence of an electric field. An insulator. However, minute displacements of positive and negative charges in opposite directions are possible. A dielectric in which this charge displacement has taken place is said to be polarized.
Far field. The area sufficiently far from an antenna where the gain pattern is essentially independent of distance. In the far field, the power of an electromagnetic wave traveling in free space drops off as the square of the distance from the transmitting antenna.
Fresnel reflection coefficient. A measure of the degree of reflection of an electromagnetic wave at the interface between two media. Dependent on the properties of the media, the polarization of the wave, and the angle of incidence.
Gain. For a transmitting antenna, the ratio of the radiation intensity in a given direction to the radiation that would be obtained if the power accepted by the antenna was radiated isotropically. For a receiving antenna, it is the ratio of the power delivered by the antenna in response to a signal arriving from a given direction compared to that delivered by a hypothetical isotropic reference antenna.
Gain (amplitude) pattern. The spatial variation of an antenna’s gain.
Human phantoms. Models of parts of the human body used in engineering, science, and medical studies designed to mimic a particular physical, chemical, or electrical behavior.
Impedance. The complex ratio of the voltage to the current in an alternating current circuit. Sometimes called complex resistance in which case the absolute value of the complex resistance is called the impedance. Units are ohms.
Lossy material. A material in which a significant amount of the energy of a propagating electromagnetic wave is absorbed (dissipated) per unit distance traveled by the wave.
Near field. The region around an antenna within a few wavelengths where there are strong inductive and capacitive effects from the currents and charges in an antenna that cause electromagnetic components not to behave like far-field radiation. Within the radiating part of the near field, the gain pattern is dependent on the distance from the antenna.
Polarization. The sense of vibration of electromagnetic radiation. There are two main types of polarization: linear, in which the radiating wave’s electric field vector is confined to a particular direction (typically vertical or horizontal); and circular, where the electric field vector rotates as the wave propagates through space. Depending on the sense of rotation, a signal’s waves may be left-hand or, as with GPS signals, right-hand circularly polarized. For maximum response, the polarization of a receiving antenna should match the polarization of the signals.
(Absolute) Permittivity. A measure of how an electric field affects, and is affected by, a dielectric material. In a sense, it describes a material’s ability to transmit (or “permit”) an electric field. Since the response of most materials to external fields generally depends on the frequency of the field, permittivity is expressed as a complex quantity with real and imaginary components as a function of frequency. Units are farads per meter.
Relative permittivity. The ratio of the permittivity of a material to that of free space or a vacuum. Also called the dielectric constant. Unitless.
Return loss. A measure of the effectiveness of power delivery from a transmission line to a load such as an antenna or vice versa. If the power incident on an antenna is Pin and the power reflected back to the source is Pref, the degree of mismatch between the incident and reflected power in the traveling waves is given by the ratio Pin/Pref. Units are dB. Functionally related to the Fresnel reflection coefficients and VSWR.
Voltage standing wave ratio (VSWR). A measure of the size of the reflected waves in a transmission line due to impedance mismatches between the line and a connected antenna. The ratio of the maximum voltage along the line to the minimum voltage along the line. Ideally, an antenna should have a VSWR value of unity.
FIGURE 1. Change in the reactive portion of the impedance of a patch antenna versus separation distance between the antenna element and imitation human skin (Courtesy, Buckley et al., 2010; see Further Reading).
The figure provides an example of the impedance for a patch antenna plotted against the separation distance of a simulated human wrist. When mounted directly on the user’s skin surface, this specific antenna gains significant reactive impedance that results in a large voltage standing wave ratio (VSWR) with the network.
A second effect of antenna proximity to human skin is the alteration of the center frequency, as well as the alteration of the antenna bandwidth. Depending on the bandwidth of the signal of interest, the bandwidth of the antenna element, and the degree of center-frequency shifting and bandwidth loss experienced, these factors can contribute to significant loss of received power.
Thirdly, it is important to note that in some configurations, a “lossy” medium adjacent to an antenna may improve the apparent performance of the antenna due to changes in its gain pattern that result in better receive or transmit performance for a given azimuth and elevation angle.
For any application in which the antenna may be either in free space or directly adjacent to a lossy medium such as a human body, the use of balanced antennas is recommended. The image current of a balanced antenna is contained within complementary structures of the antenna itself, not within the casing or adjacent material of the antenna, therefore making the antenna much less likely to interact with surrounding media.
Fourth, the close proximity of a reflective material increases close-range multipath. If the distance between the reflector (that is, skin) and the antenna is close to half a wavelength, giving a 180º phase shift of the carrier, deconstructive interference can occur. There are several factors that contribute to this including the back lobe of the antenna gain pattern, reflection coefficient of the skin beneath the antenna, and the incident angle of the incoming ray. Approximation via simple ray tracing becomes dauntingly complex due to the variation of the antenna properties listed above, resulting from detuning. Therefore, observation of the effect becomes easier than modeling an incoming ray and its multipath counterparts.
Phantom Body Simulation
To conduct an assessment of the impact of the human body on the radiation patterns of diverse antennas in the context of tracking GNSS signals, a human body phantom has been designed for collecting the experimental data. Variations of the locations and orientations of the antenna rigidly mounted on a human shoulder, head, or any other locations would render the repeatability and comparison of the collected data hardly feasible. Furthermore, the distance that separates the antenna from the human body surface could only be precisely controlled using an artificial modeling of the human body. Therefore, a human body phantom is required for productive analysis.
Because the human body is mainly composed of water, the presence of human tissue in the vicinity of the antenna introduces an absorption and reflective effect that alters the performance of the antenna. Different mathematical models have been developed for representing the different component combinations of a human body. Based on the study of numerous women and men of different ages and sizes, a classic model predicting the fat-free mass of a person has been developed and assumes that 73 percent of a human body consists of water. Looking at the elemental composition in the human body, it can be found that a concentration of 7 grams of salt per liter of water provides an acceptable modeling of the human tissues. Complex shapes of the human body are used for modeling more precisely the layered structure of the human tissues using either a more realistic human phantom or a more detailed model comprising the extensive data on the dielectric properties of each layer constituting the human tissues of interest. For context of this study, the phantom was kept simple and was made of a large plastic container filled with a 7 percent concentration of a saline solution.
The radiative transfer of the human body phantom on the reception of GNSS signals can be evaluated through the understanding of the dielectric permittivity of the solution. Different models, including the Wagner, Debye, Cole & Cole, or Fricke, are commonly used for studying the dielectric behavior of biological tissues. The Debye model gives the permittivity of an aqueous saline solution of salinity, S, at a fixed temperature, t, as
(1)
where
is the angular frequency (Hz),
εi equals 8.8419 ×10-12 (farads per meter),
τ is the relaxation time (seconds),
σ is the ionic conductivity of the dissolved salts (mhos per meter), and
ε0 and ε∞ are the static and high frequency dielectric constants.
Equation (1) gives the dielectric proprieties of the human phantom solution for a specific temperature, saline concentration, and temperature. The experiments we conducted and report on in this article lasted several days and were conducted outside, which unfortunately resulted in temperature fluctuations. Consequently, the 7 percent saline solution over the temperature range of 11º to 31º C for L1 (1575.42 MHz) results in a 9 percent variation of permittivity. As shown in FIGURE 2, the dielectric constant over the experimental temperature range is in the interval [74.6, 81.9]. Because the variation is small, the permittivity value can be closely approximated to a mean value of 78.
FIGURE 2. Real part of the permittivity of the human body phantom as a function of temperature for the GPS L1 frequency.
Reflection Coefficient of the Phantom Body
The Fresnel reflection coefficients for a smooth flat surface depend on frequency, the incident angle, polarization, and ground characteristics. Since the container is full of salted water it can also be considered a reflective surface.
The relative permittivity of the saline solution given in Equation (1) can be reformatted as
(2)
The reflection coefficients with vertical and horizontal polarizations, respectively, of the electromagnetic wave on the surface of the saline water are given by the following Fresnel equations:
(3)
(4)
where Rv and Rhare the vertical and horizontal polarized reflection coefficients, respectively, and θ is the incident angle.
Assuming that the water surface is flat and infinite, Equations (3) and (4) are plotted against the incident angle in FIGURE 3. The reflection coefficients were estimated using a mean temperature of 21°C, a salt concentration of 7 percent and at the L1 frequency.
FIGURE 3. Fresnel coefficient for L1 considering a flat surface of salted water.
While the saline solution of the human phantom has an angle of incidence and direction of polarization dependent on reflectivity, the fact that the GPS carrier is circularly polarized must be considered. Due to the circular polarization of the carrier and that of most antenna elements intended for GPS use, the received signal strength of the reflected wave will always appear to be equal to or higher than that of the reflected portion of the horizontal polarization.
Test Setup
To evaluate the change in gain pattern as function of distance from the phantom, we collected 24-hour data segments. These segments allowed the receiver to observe all satellites. A high-performance GPS L1 receiver module evaluation kit was used with two antennas. The first was a patch antenna while the second was a quadrifilar helix antenna. FIGURE 4 shows both antennas without their coverings. Each antenna has a built-in low noise amplifier (LNA). The antenna specifications are listed in TABLE 1.
FIGURE 4. Patch (above) and quadrifilar (below) antennas used in the tests.TABLE 1. Antenna specifications.
A water container holding the saline solution was placed on the roof of a building as shown in FIGURE 5. The container had a slight inclination to move a small air pocket to the corner of the container away from the antenna. After a successful 24-hour data collection period, the antenna was supported by a small plastic box and oriented in the same direction. Six vertical distances were selected, namely 0, 11, 22, 30, 41, and 52 millimeters.
FIGURE 5. Data collection with patch antenna fixed to phantom body.
The gain pattern as measured by the C/N0 values of the path antenna is shown in FIGURE 6. In general, the largest effect is seen near the zenith where the power decreased by 10–15 dB when the antenna was 22 millimeters from the phantom body. It is also observed that the effect is maximized at 22 millimeters, and then reverts back to near normal operation at 52 millimeters. Additionally, at lower elevation angles (< 30º), the gain behaves more linearly, where the largest distance has the least gain, while the smallest distance has the most gain. The effect of the phantom body appears to flatten the gain pattern.
The pattern shown in Figure 6 shows the effect of the proximity to the phantom body over all elevation angles. However, a prominent pattern emerges for measurements made at elevation angles of 45º and 85º. In the case of a 22-millimeter antenna distance from the body, a significant power decrease occurs. For satellites with an 85º elevation angle, nearly 8 dB is lost compared to 5 dB loss at a 45º elevation angle.
FIGURE 6. Gain pattern of the patch antenna as measured by the measured C/N0 at all elevation angles as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º].FIGURE 7 provides the trend as a function of distance from the body. The trend of the power loss at 22 millimeters is common on all measurements, albeit more significant for higher-elevation-angle satellites. For satellite measurements made at an 85º elevation angle, the power varies by 12 dB. When all measurements are considered, which includes more frequent lower-elevation-angle satellite measurements and the fact that the gain pattern deviates significantly at higher elevation angles (as shown in Figure 6), the fluctuation is less prominent.
FIGURE 7. Mean C/N0 measurements of the patch antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body.
To assess the cause of the impact, we removed the phantom and replaced it with a flat aluminum reflector placed beneath the antenna. The antenna was then placed at the same distances above the reflector as previously. Since the gain pattern had been established and this test was to observe the effect of the reflector, only 60 seconds of data was collected at each distance.
FIGURE 8 provides the change in C/N0 for two tests, which has a comparable trend to that of Figure 7. From the corroboration of the two tests, it appears that the salt water provides similar multipath effects to that of the aluminum sheet. The power loss is then attributed to destructive interference.
FIGURE 8. Mean C/N0 measurements (over 60 seconds) of satellite PRN 8 with 85º elevation angle when placed above an aluminum reflector.
Similar data collections were conducted with the quadrifilar helix in order to assess its ability to perform close to the human phantom. The quadrifilar antenna has the LNA circuitry vertically below the antenna and therefore was placed horizontally on the water container. FIGURE 9 shows its gain pattern. The overall C/N0 is lower but is subject to less variation compared to that of the patch antenna. In general, we noticed lower C/N0 values with the quadrifilar antenna, regardless of the environment and despite the LNA having 5 dB more amplification. Some moderate variations of up to 10 dB appear on the east side of the antenna (zenith angle [0º, 90º]), but overall the pattern appears to be more regular.
FIGURE 9. Gain pattern of the quadrifilar antenna as measured by the C/N0 of all measurements as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º].The overall power variation was assessed in a similar method. FIGURE 10 shows cubic-like functions with 3-dB variations. There is also no consistent downward power loss trend at 22 millimeters as observed with the patch antenna. As expected, due to the balanced nature of the quadrifilar antenna, the degree of apparent power loss caused by adjacent material is substantially lower compared to the patch antenna. While the peak level of power received is not as high as that experienced with the patch antenna, the consistency of the received power level is better.
FIGURE 10. Mean C/N0 measurements of the quadrifilar antenna from all measurements and those only at 45º and 85º elevation angles as a function of antenna distance from the body.
Conclusions
We have investigated the impact of the proximity of the human body on received signal power associated with operation of L1 GPS antennas through experimental tests. GPS signals have been collected using two different antenna types (a patch antenna and a quadrifilar helix antenna), placed on a human body phantom with different separation distances. A strong relationship between these distances and the averaged received signal power has been observed for both antennas with overall lower C/N0 values for the quadrifilar antenna. The largest attenuation is not observed when the antenna is directly adjacent to the user body but when it is about 22 millimeters above it. We found that the attenuation mainly results from destructive interference due to multipath. These results suggest that body-mounted GPS antennas should be directly in contact with the user’s body for achieving better tracking performance. Our future research will include theoretically assessing the experimental results for better understanding of the underlying effects.
Acknowledgments
This article is based on the paper “GNSS Antenna-Human Body Interaction” presented at ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 19–23, 2011. The authors would like to thank Prof. Ron Johnston, Dept. of Electrical and Computer Engineering, The University of Calgary, for his insight and consultation in preparing that paper. We thank John Buckley, Tyndall National Institute, Ireland, and his co-authors for permission to use Figure 1, a version of which appears in “The Detuning Effects of a Wrist-Worn Antenna and Design of a Custom Antenna Measurement System” (see Further Reading).
Manufacturers
The tests discussed in this article used a u-blox AG EVK-6T evaluation kit using a LEA-6T L1 GPS module, an Allis Communication Co. Ltd. M827B active L1 patch antenna, and a Sarantel Ltd. SL1206 active L1 quadrifilar helix antenna.
Jared B. Bancroft is a senior research engineer in the Position, Location And Navigation (PLAN) Group in the Department of Geomatics Engineering at The University of Calgary in Calgary, Alberta, Canada. He received his Ph.D. in geomatics engineering in 2010 and has worked in the area of navigation since 2004. Dr. Bancroft’s research interests include pedestrian and vehicular navigation through data fusion of sensors and satellite navigation data.
Valérie Renaudin is a senior research associate in the PLAN Group. She received an M.S. in geomatics engineering from the Ecole Supérieure des Géomètres et Topographes, France, in 1999 and a doctorate in geomatics engineering from the Ecole Polytechnique Fédérale de Lausanne, in 2009. She was previously the technical director at Swissat AG. Her research interests include low-cost sensors, hybridization techniques, magnetometers, and indoor navigation.
AidenMorrison is a senior research associate in the PLAN Group. He received his B.Eng. in electrical engineering from Ryerson University, Canada, in 2006 and a Ph.D. in geomatics engineering from The University of Calgary in 2010. His research interests include development of integrated navigation systems.
Gérard Lachapelle holds a Canada Research Chair in Wireless Location in the Department of Geomatics Engineering at The University of Calgary, where he has been a professor since 1988 and heads the PLAN Group. He has been involved in a multitude of GNSS R&D projects since 1980, ranging from RTK positioning to indoor location and GNSS signal processing enhancements.
Further Reading
• Previous Work by Authors
“GPS Observability and Availability for Various Antenna Locations on the Human Body” by J.B. Bancroft, G. Lachapelle, T. Williams, and J. Garrett in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 2941–2951.
• Interaction between Receiving Antennas and Human Body Parts
“The Detuning Effects of a Wrist-Worn Antenna and Design of a Custom Antenna Measurement System” by J. Buckley, K.G. McCarthy, B. O’Flynn, and C. O’Mathuna in Proceedings of the 40th European Microwave Conference, Paris, France, 28–30 September 2010, pp. 1738-1741.
“One-Layer GPS Antennas Perform Well Near a Human Body” by T. Kellomaki, J. Heikkinen, and M. Kivikoski in Proceedings of EuCAP 2007, the Second European Conference on Antennas and Propagation, Edinburgh, Scotland, November 11–16, 2007, 6 pp.
“Effects of Human Body Interference on the Performance of a GPS Antenna” by M. Ur Rehman, Y. Gao, X. Chen, C.G. Parini, and Z. Ying in Proceedings of EuCAP 2007, the Second European Conference on Antennas and Propagation, Edinburgh, Scotland, November 11–16, 2007, 4 pp.
• Wearable Antennas
“Design of a Protective Garment GPS Antenna” by L. Vallozzi, W. Vadendriessche, H. Rogier, C. Hertleer, and M.L. Scarpello in Microwave and Optical Technology Letters, Vol. 51, No. 6, June 2009, pp. 1504–1508, doi: 10.1002/mop.24372.
“Wearable Antennas in the Vicinity of Human Body” by P. Salonen, Y. Rahmat-Samii, and M. Kivikoski in Proceedings of the IEEE Antennas and Propagation Society International Symposium, Monterey, California, June 20–26, 2004, pp. 467–470, doi: 10.1109/APS.2004.1329675.
“A Small Planar Inverted-F Antenna for Wearable Applications” by P. Salonen, L. Sydänheimo, M. Keskilammi, and M. Kivikoski in Digest of Papers, the Third International Symposium on Wearable Computers, San Francisco, California, October 18–19, 1999, pp. 95–100, doi: 10.1109/ISWC.1999.806679.
• Dielectric Properties of Human Tissue and Sea Water
“New Permittivity Measurements of Seawater” by W. Ellison, A. Balana, G. Delbos, K. Lamkaouchi, L. Ey, C. Guillou, and C. Prigent in Radio Science, Vol. 33, No. 3, 1998, pp. 639–648, doi: 10.1029/97RS02223.
“Studies on Body Composition. III. The Body Water and Chemically Combined Nitrogen Content in Relation to Fat Content” by N. Pacen and E.N. Rathurn in Journal of Biological Chemistry, Vol. 158, 1945, pp. 685–691.
• Human Phantoms
“Solid Phantoms for Evaluation of Interactions Between the Human Body and Antennas” by K. Ito and H. Kawai in Proceedings of IWAT 2005, the 2005 IEEE International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, Singapore, March 7–9, 2005, pp. 41–44, doi: 10.1109/IWAT.2005.1460993.
“A High-Precision Real Human Phantom for EM Evaluation of Handheld Terminals in a Talk Situation” by K. Ogawa, T. Matsuyoshi, H. Iwai, and N. Hatakenaka in 2001 Digest, IEEE Antennas and Propagation Society International Symposium, Boston, Massachusetts, July 8–13, 2001, Vol. 2, pp. 68–71, doi: 10.1109/APS.2001.959623.
By Saeed Daneshmand, Ali Jafarnia-Jahromi, Ali Broumandan, and Gérard Lachapelle
Most anti-spoofing techniques are computationally complicated or limited to a specific spoofing scenario. A new approach uses a two-antenna array to steer a null toward the direction of the spoofing signals, taking advantage of the spatial filtering and the periodicity of the authentic and spoofing signals. It requires neither antenna-array calibration nor a spoofing detection block, and can be employed as an inline anti-spoofing module at the input of conventional GPS receivers.
GNSS signals are highly vulnerable to in-band interference such as jamming and spoofing. Spoofing is an intentional interfering signal that aims to coerce GNSS receivers into generating false position/navigation solutions. A spoofing attack is, potentially, significantly more hazardous than jamming since the target receiver is not aware of this threat. In recent years, implementation of software receiver-based spoofers has become feasible due to rapid advances with software-defined radio (SDR) technology. Therefore, spoofing countermeasures have attracted significant interest in the GNSS community.
Most of the recently proposed anti-spoofing techniques focus on spoofing detection rather than on spoofing mitigation. Furthermore, most of these techniques are either restricted to specific spoofing scenarios or impose high computational complexity on receiver operation.
Due to the logistical limitations, spoofing transmitters often transmit several pseudorandom noise codes (PRNs) from the same antenna, while the authentic PRNs are transmitted from different satellites from different directions. This scenario is shown in Figure 1. In addition, to provide an effective spoofing attack, the individual spoofing PRNs should be as powerful as their authentic peers. Therefore, overall spatial energy of the spoofing signals, which is coming from one direction, is higher than other incident signals. Based on this common feature of the spoofing signals, we propose an effective null-steering approach to set up a countermeasure against spoofing attacks. This method employs a low-complexity processing technique to simultaneously de-spread the different incident signals and extract their spatial energy. Afterwards, a null is steered toward the direction where signals with the highest amount of energy impinge on the double-antenna array. One of the benefits of this method is that it does not require array calibration or the knowledge of the array configuration, which are the main limitations of antenna-array processing techniques.
Processing Method
The block diagram of the proposed method is shown in Figure 2. Without loss of generality, assume that s(t) is the received spoofing signal at the first antenna.
Figure 2. Operational block diagram of proposed technique.
The impinging signal at the second antenna can be modeled by , where θs and μ signify the spatial phase and gain difference between the two channels, respectively. As mentioned before, the spoofer transmits several PRNs from the same direction while the authentic signals are transmitted from different directions. Therefore, θs is the same for all the spoofing signals. However, the incident authentic signals impose different spatial phase differences. In other words, the dominant spatial energy is coming from the spoofing direction. Thus, by multiplying the conjugate of the first channel signals to that of the second channel and then applying a summation over N samples, θs can be estimated as (1)
where r1 and r2 are the complex baseband models of the received signals at the first and the second channels respectively, and Ts is the sampling duration. In (1), θs can be estimated considering the fact that the authentic terms are summed up non-constructively while the spoofing terms are combined constructively, and all other crosscorrelation and noise terms are significantly reduced after filtering. For estimating μ, the signal of each channel is multiplied by its conjugate in the next epoch to prevent noise amplification. It can easily be shown that μ can be estimated as (2)
where T is the pseudorandom code period. Having and a proper gain can be applied to the second channel in order to mitigate the spoofing signals by adding them destructively as (3)
Analyses and Simulation Results
We have carried out simulations for the case of 10 authentic and 10 spoofing GPS signals being transmitted at the same time. The authentic sources were randomly distributed over different azimuth and elevation angles, while all spoofing signals were transmitted from the same direction at azimuth and elevation of 45 degrees. A random code delay and Doppler frequency shift were assigned to each PRN. The average power of the authentic and the spoofing PRNs were –158.5 dBW and –156.5 dBW, respectively.
Figure 3 shows the 3D beam pattern generated by the proposed spoofing mitigation technique. The green lines show the authentic signals coming from different directions, and the red line represents the spoofing signals. As shown, the beam pattern’s null is steered toward the spoofing direction.
Figure 3. Null steering toward the spoofer signals.
In Figure 4, the array gain of the previous simulation has been plotted versus the azimuth and elevation angles. Note that the double-antenna anti-spoofing technique significantly attenuates the spoofer signals. This attenuation is about 11 dB in this case. Hence, after mitigation, the average injected spoofing power is reduced to –167.5 dBW for each PRN. As shown in Figure 4, the double-antenna process has an inherent array gain that can amplify the authentic signals. However, due to the presence of the cone of ambiguity in the two-antenna array beam pattern, the power of some authentic satellites that are located in the attenuation cone might be also reduced.
Figure 4. Array gain with respect to azimuth and elevation.
Monte Carlo simulations were then performed over 1,000 runs for different spoofing power levels. The transmitted direction, the code delay, and the Doppler frequency shift of the spoofing and authentic signals were changed during each run of the simulation. Figure 5 shows the average signal to interference-plus-noise ratio (SINR) of the authentic and the spoofing signals as a function of the average input spoofing power for both the single antenna and the proposed double antenna processes. A typical detection SINR threshold corresponding to PFA=10-3 also has been shown in this figure.
Figure 5. Authentic and spoofed SINR variations as a function of average spoofing power.
In the case of the single antenna receiver, the SINR of the authentic signals decreases as the input spoofing power increases. This is because of the receiver noise-floor increase due to the cross-correlation terms caused by the higher power spoofing signals. However, the average SINR of the spoofing signals increases as the power of the spoofing PRNs increase.
For example, when the average input spoofing power is –150 dBW, the authentic SINR for the single-antenna process is under the detection threshold, while the SINR of the spoofing signal is above this threshold. However, by considering the proposed beamforming method, as the spoofing power increases, the SINR of the authentic signal almost remains constant, while the spoofing SINR is always far below the detection threshold.
Hence, the proposed null-steering method not only attenuates the spoofing signals but also significantly reduces the spoofing cross-correlation terms that increase the receiver noise floor. Early real-data processing verifies the theoretical findings and shows that the proposed method indeed is applicable to real-world spoofing scenarios.
Conclusions
The method proposed herein is implemented before the despreading process; hence, it significantly decreases the computational complexity of the receiver process. Furthermore, the method does not require array calibration, which is the common burden with array-processing techniques.
These features make it suitable for real-time applications and, thus, it can be either employed as a pre-processing unit for conventional GPS receivers or easily integrated into next-generation GPS receivers. Considering the initial experimental results, the required antenna spacing for a proper anti-spoofing scenario is about a half carrier wavelength. Hence, the proposed anti-spoofing method can be integrated into handheld devices.
The proposed technique can also be easily extended to other GNSS signal structures. Further analyses and tests in different real-world scenarios are ongoing to further assess the effectiveness of the method.
Saeed Daneshmand is a Ph.D. student in the Position, Location, and Navigation (PLAN) group in the Department of Geomatics Engineering at the University of Calgary. His research focuses on GNSS interference and multipath mitigation using array processing.
Ali Jafarnia-Jahromi is a Ph.D. student in the PLAN group at the University of Calgary. His research focuses on GNSS spoofing detection and mitigation techniques.
Ali Broumandan received his Ph.D. degree from Department of Geomatics Engineering, University of Calgary, Canada. He is a senior research associate/post-doctoral fellow in the PLAN group at the University.
Gérard Lachapelle holds a Canada Research Chair in wireless location In the Department of Geomatics Engineering at the University of Calgary in Alberta, Canada, and is a member of GPS World’s Editorial Advisory Board.
By Chaminda Basnayake, Tom Williams, Paul Alves, and Gérard Lachapelle
Communication-enabled vehicle safety has the potential to change transportation’s future, particularly vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), collectively represented as V2X. An automakers’ consortium conducted extensive field trials to determine GNSS service availability and accuracy for the V2X challenge.
V2X can include applications based on communications between any two or more entities on the road. Of all the potential V2X applications, V2V applications probably lead the way in terms of maturity of prototype development and test efforts. General Motors (GM) demonstrated the first working prototype V2V system in 2005. Information on further industry collaborative efforts in V2V system developments can be found at the U.S. Department of Transportation’s (DOT’s) IntelliDrive website. While a multitude of applications could be developed based on V2I capability, most of the related system prototype development efforts have taken place under the DOT’s Cooperative Intersection Collision Avoidance (CICAS) program.
Driving environments encountered in testing. Clockwise from top left: deep urban, urban thruway, local roads, mountains.
Accuracy Requirements
In terms of positioning accuracy requirements, Vehicle Safety Communications-Applications (VSC-A) prototype system capabilities as well as all V2X applications can be classified as:
Which Road. In this case, accuracy is only required to the extent of identifying the road traveled. For instance, if a vehicle is in a service road parallel to a freeway, knowing that it is on the service road and not on the freeway is sufficient. The need of a typical vehicle navigation device is another good example of this requirement category. The typical accuracy requirement for this case is better than 5 meters. However, this could be a relative accuracy requirement for certain applications. For instance, in a V2V scenario, one vehicle may only need to know if the other is on the same road or not, while in the absolute sense both vehicles could be in error by more than 5 meters. For V2I applications, however, this becomes an absolute accuracy requirement, as the infrastructure is always mapped and identified with respect to a global coordinate frame.
Which Lane. This accuracy level enables applications to identify other entities with lane level resolution. The typical requirement is 1.5 meters or better, which approximately corresponds to half of a lane width. A blind-spot advisor is a good example that requires this accuracy.
Where-in-Lane. This accuracy level enables the relative positioning of entities to better than 1 meter. Further refinements of blind-spot advisor-like applications are examples.
Availability Requirements
GNSS as a line-of-sight technology has obvious limitations in certain environments, and these limitations are well understood by the GNSS community. The focus of this study was to understand the limitations associated with a GNSS-only V2X solution such that requirements for augmentation technologies can be defined. Therefore, no availability requirements were set for the system; estimating availability of a GNSS-only solution was the goal.
Why So Complicated? At first glance, what needs to be done is straightforward; all V2X-capable entities need to be aware of each other’s positions. Hence, if all entities transmit their own location with respect to the same coordinate system, the problem is solved. Unfortunately, it’s not that simple.
Designing the system so that hundreds of entities, potentially using all sorts of GNSS software and hardware, can work together presents a significant challenge. This includes keeping backward compatibility way out into the future.
Even within the same receiver make and type, inclusion of a particular satellite in the solution of one vehicle can significantly affect the solution difference between vehicles. Inclusion of SBAS also contributes as a differentiator. In a V2X scenario, out of two adjacent vehicles, one vehicle may use SBAS while the other may not, due to hardware configuration or visibility. If none of the above situations occurred and everything else were ideal, transmitting just the current horizontal position of a V2X entity over-the-air (OTA) would be sufficient to do everything needed.
V2X thus requires a positioning system architecture that minimizes the impact of these complications and many other potential compatibility issues. Major system design considerations include:
Performance Requirements. The system must provide relative positioning accuracy that fits Which Road, Which Lane, or Where-in-Lane category and should identify the solution quality. For instance, a vehicle on a freeway with relatively open sky view may function in the Which Lane mode and may transition to Which Road mode as it enters an urban area with sky visibility limitations.
Deployment Constraints. The system must be affordable for automotive applications. This may also include considerations such as antenna placement, processing resource requirements, and power requirements.
Bandwidth Constraints. The volume of data transmission constitutes a major consideration for OTA communications. While some methods manage communication range and frequency as a way of optimally using the communication channels, keeping the OTA data volume to a minimum by design was a goal.
Study Goals
This study investigated the performance of two relative positioning methods: DPOS, a method of using the difference in position reported by two entities to calculate the 3D separation between the points; and real-time kinematic (RTK). While there are many other possible relative positioning methods, these two were selected as they collectively represent the most desirable availability and accuracy performance. In DPOS, vehicle coordinates are transmitted between vehicles in order for position differences between vehicles to be derived at each vehicle. In RTK, raw code and carrier-phase data is transmitted between vehicles, and the inter-vehicle position differences are calculated using RTK software in either fixed or float carrier-phase ambiguity mode at each vehicle. The RTK method is more intensive both from a data transmission and computational aspect, but retains only common satellites in the solution, eliminating the problem described earlier. Its use of carrier-phase measurements also makes it more accurate.
The study included two GPS receiver types. The first, a single-frequency L1 automotive-grade receiver, is identified as Type B receiver in this study. The second, identified as Type A, was of a higher quality with proprietary multipath mitigation technologies. Both receivers were capable of using WAAS support. Receiver B also allowed the user to reject selected satellites from its solution. These two devices were selected as they were capable of supporting both processing methods, and represent on the one hand an existing automotive-grade receiver, and on the other hand one that is expected to be a good representation of a product with technologies available for automotive deployment a few years from now.
Specific study goals were:
Accuracy performance of DPOS and RTK methods when all vehicles use same GPS receiver type.
Same when a receiver type or a receiver configuration mix is used.
Dependency of the accuracy performance on the driving environment.
Solution availability with same receiver and mix receiver combinations.
Implications of non-continuous V2I coverage.
Prototype System
The system prototype (Figure 1) used for the study was a replica of the prototype relative positioning system implemented in the VSC-A project. It consists of a dedicated short-range communicatin (DSRC) interface with a DSRC radio, a GPS receiver/relative positioning module, and a sensor data handler.
In operation, a vehicle generates its own location information and GPS raw data in RTCM format and shares this data with other vehicles. OTA messaging was done using the SAE J2735 messages set with GPS raw data in RTCM format attached as optional data. As shown in Figure 1, RTCM v3 1002 messages were used to exchange VSC-A data. The system was also capable of using RTCM v3 messages 1001 & 1005 for V2I operation. The DPOS relative positioning logic was implemented in the sensor data handler, while the RTK implementation was done in a separate relative positioning module. This module takes in local and remote 1002 messages and outputs RTK data to the sensor data handler. Applications could access both RTK and DPOS relative positioning information from the sensor data handler.
Vehicle Setup. Two vehicles were used for the V2X data collection. Four different GPS L1-only test receiver types were installed on each vehicle:
AW: high-quality receiver using WAAS corrections.
BW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections enabled.
BNW: high-sensitivity automotive-grade receiver with WAAS ranging and corrections disabled.
B24W: high-sensitivity automotive-grade receiver using a maximum of the four primary satellites in each of the six planes (minimum guaranteed constellation) and with WAAS ranging and corrections enabled.
As shown in Figure 2, the AW and B type receivers were connected to different GNSS antennas. These antennas were mounted on roof-racks attached to the vehicles (see Photo). The patch antenna for the Type B receivers was mounted on an aluminum-topped wooden pedestal to bring it to approximately the same height as that used by the AW receivers, to provide a ground plane and to prevent shading from other equipment on the roof-racks. The spacing between the antennas was accounted for in all analysis.
Figure 2. High-level V2V hardware setup on each of the two test vehicles.
Figure 2 also shows that only three of the four test receivers, AW, BW, and BNW, were connected to the computer that ran the RTK software. This computer calculated the inter-vehicle vector (IVV) using information exchanged over the DSRC radio link in real time. The vehicles each had a designated base relative to which the IVV was calculated; for Vehicle 1 it was BW and for Vehicle 2 it was AW. Thus the computer on each vehicle calculated three instances of the IVV, for example, the computer on Vehicle 1 calculated BW1–BW2, BW1–BNW2, and BW1–AW2 (where Ri denotes the receiver of type R on vehicle i).
Transmission and reception of data between the two vehicles required for the IVV RTK calculations were achieved using wave radio modules with two magnetically mounted 802.11p antennas on each vehicle for redundancy. During testing, Vehicle 1 generally followed Vehicle 2. To minimize potential interference of roof-mounted instruments on between-vehicle communications, the antennas on Vehicle 1 were located close to the front of the roof, while those on Vehicle 2 were located close to the rear of the roof. In each case, 15 centimeters of roof space were left to provide ground planes for the antennas.
We used the single-point navigation solutions logged from each test receiver to calculate the IVV for each receiver combination using the DPOS method in post-processing. No real-time data transfer between the vehicles was used for this method.
Reference values of the IVV were calculated in post-processing using both geodetic grade GPS/GLONASS L1/L2 receivers and GPS/INS integrated systems in differential mode. Both were connected to the antenna used by the AW receiver. Differential GPS calculations were enabled by using stationary receivers with antennas at precisely known WGS84 locations on top of a building at the University of Calgary.
Two study vehicles with antennas attached to the roof-racks.
Test Scenarios
V2V data was collected in and around the city of Calgary in August 2009. In the majority of the tests, Vehicle 1 followed Vehicle 2 with a separation of less than 300 meters, the stated effective range of the DSRC link. For most tests the inter-vehicle separation was between 30 and 150 meters. Some driving environments forced modifications of the default behavior; for example, on highways, vehicles moved in between the two test vehicles, necessitating lane changes. Approximately 52 hours of data was collected over 12 days. After rejecting data due to various faults such as reference-system malfunction, more than 45 hours of data remained.
Data was collected in the seven test environments listed in Table 1. These environments were selected in accordance with Federal Highway Administration descriptions. Each environment provided different challenges for GNSS-based positioning. Obviously the deep urban environment was challenging because the reduced number of visible satellites and the large amount of multipath meant that navigation solutions were both rare and of poor quality. As another example, the mountain environment was interesting because often almost half the sky was occluded by trees on the mountain side, leading to an asymmetrical visible GPS satellite constellation with the associated solution degradation. The photos at the beginning of this article show selected driving environments encountered during testing.
Table 1. Description of driving environments used in V2V tests.
V2V Solution Accuracy. Positioning accuracy of the individual receiver was first investigated to estimate the V2V relative positioning accuracy when using the DPOS method. This was done for the entire dataset.
Figure 3A shows a representative freeway dataset to illustrate overall trends: the absolute 2D mean position errors observed from all eight GPS receivers used in both vehicles. The first set of four receivers shown were the AW, BW, BNW, B24W receivers in the first vehicle (V1), and the second set of receivers were the same type in the second vehicle (V2). As a general trend, Type A receivers provided better absolute accuracy meeting the Which Lane accuracy, whereas the Type B receivers provided Which Road accuracy. Also, the use of WAAS with receiver Type B has yielded some absolute accuracy improvement. Limiting the constellation to 24 (B24W) did not significantly degrade accuracy in this case.
As a second step, V2V relative accuracy when the same receiver type was used was estimated, and the mean errors are shown in Figure 3B. Based on the mean error for each pair, all four receiver pairs were able to provide Where-in-Lane relative position accuracy. The geodetic grade Type A receiver pair (AW–AW) yields the best relative accuracy at around 0.5 meters relative 2D error. In comparison with the mean absolute errors, the V2V relative accuracy is greatly improved as a result of cancellation of correlated errors, indicating a high degree of correlation of absolute errors in receivers under these test conditions.
The relative accuracy with mixed receiver types or configurations was also estimated. With r
espect to receiver type mixes, the Type A receiver from vehicle 1 was used with the three Type B receivers in vehicle 2, yielding three combinations as AW–BW, AW–BNW, and AW–B24W. Mean error statistics for these three combinations and the combination of BW from vehicle 1 and B24W from the second vehicle are shown in Figure 3C. In comparison to the same type receiver pairing, this shows much larger mean errors. For instance, for all AW receiver mixes, the mean relative error is around 2 meters. Therefore, it is fair to conclude that error characteristics and modeling in the navigation solutions in receiver A and B are type-dependent, and they may not be compatible when a receiver mix is used. The BW–B24W combination does not show a significant increased mean error, indicating that the constellation difference in this test was not significant enough to result in an increased relative positioning error.
Figure 3A. Individual receiver absolute accuracy.Figure 3B. Relative accuracy with same receiver type.Figure 3C. Relative accuracy with receiver/configuration mix.
V2V Solution Availability
Availability statistics were generated for all accuracy categories (Which Road, Which Lane). At a more abstract level, solution availability statistics were also calculated for the DPOS and RTK methods. RTK solutions were defined as available whenever the software yielded a solution for that particular epoch. Data gaps in the RTK method could be caused by either communication failure due to, for example, a large truck entering the line of sight between vehicles, or one vehicle disappearing around a corner, or because insufficient observations from common satellites were available at the two vehicles. DPOS solutions, calculated in post-processing, were defined to be available whenever both receivers had observations from four or more satellites and were therefore able to calculate the necessary independent position solutions. While the two definitions of availability are not quite congruous, because only that for the RTK includes the possibility of communication failure, comparison of logs of data transmitted between the vehicles showed that out of approximately 45 hours of data, only 0.22 percent of missing RTK solutions could be attributed to failure of the DSRC link.
Figure 4 plots the distribution of GPS service outages observed by AW and BW receivers in individual vehicles in all of the test environments including deep urban. Here, as described for the DPOS method, an outage for a single receiver is identified on an epoch basis whenever the receiver has observations from less than four satellites. The total driving time included in this dataset is 45 hours and 4 minutes for each receiver. Figure 4 [deep urban] shows the same statistics for deep urban environment driving only, and this contains 1 hour and 40 minutes of driving for each receiver. The latter was selected specifically as this environment contained the most challenging conditions.
Figure 4. Distribution of GPS service outages for individual vehicles.
An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages lasted less than 30 seconds. However, when using the high-sensitivity enabled Type B receivers, 100 percent of the outages lasted less than 5 seconds. No significant outage difference is seen between the observations from the same receiver type in the two vehicles.
GPS service availability for V2V applications was calculated using two approaches for the two relative positioning methods. For the DPOS method, individual vehicle service availabilities were time-synchronized in post-mission, and V2V DPOS solution availability was estimated. Figure 5 compares V2V solution outages using both receiver types and both relative positioning methods.
Figure 5. Distribution of GPS service outages for V2V applications.
The DPOS method yields better solution availability statistics than RTK. With both receiver types, more than 95 percent of DPOS solution outages are less than 10 seconds. With the RTK method, relatively longer outages were observed, especially for Type B receivers. With Type A receivers, the difference is only significant for outages shorter than 30 seconds. For Type B receivers, larger percentages of longer RTK outages were observed; this can be potentially attributed to poor carrier-phase tracking loop performance of these receivers and the impact on RTK.
Using GNSS Data
We anticipated performance issues arising from receiver type and configuration incompatibilities going into the prototype development effort. We identified use of raw GPS measurements instead of the DPOS method as one method to overcome this limitation, as the differencing techniques with measurement data guarantees correlated error cancellation. This was one reason to include the RTK capability in the prototype system. Therefore, confirming the fact that use of raw measurements eliminates the receiver type and configuration-related incompatibilities was a major goal of the study.
As discussed earlier, V2V relative position solutions using RTK were logged in real time as a part of the test setup. We compared these real-time RTK solutions and the DPOS solutions estimated in post-mission for all datasets. Figure 6 shows three cumulative probability distribution (CDF) plots generated using RTK and DPOS accuracy data from a freeway test dataset. The first CDF plot (left) shows the comparison of accuracy when both vehicles use Type A receivers with RTK and DPOS methods. The second CDF plot (center) shows the same CDFs when both vehicles use the Type B receivers. The third shows the DPOS and RTK accuracy CDFs when vehicle 1 uses Type A receiver and the other uses Type B receiver.
Figure 6 demonstrates that if higher quality GPS receivers similar to Type A are used in both vehicles, both RTK and DPOS methods would provide a solution of better than Which Lane accuracy more than 90 percent of the time. However, if Type B receivers are used, a solution with similar accuracy will only be available 60 percent of the time if the DPOS method is used for relative positioning of the vehicles. If the RTK method is used, this availability can be increased up to 90 percent.
The performance difference between the two methods becomes even more prominent when the two vehicles use a mix of receiver types. In the right-most CDF of Figure 6, a solution with Which Lane accuracy is only available 30 percent of the time if DPOS method is used with the mixed receiver configuration. The RTK solution availability still remains around 90 percent even with the mixed configuration. This confirms that use of measurement data eliminates some of the limitations associated with the DPOS method.
Comparison of only the RTK performance between all three CDFs in Figure 6 shows that RTK V2V performance is only limited by the worst-performing receiver in the receiver combination. Out of the three CDFs, the middle (both vehicles using Type B) and the right (Type A and B mix) CDFs have almost identical RTK performance curves. Given that the RTK curve with both using Type A receivers shows much better performance, it is fair to conclude that in the mixed-receiver case, the RTK curve is limited by the performance of th
e Type B receiver. Figure 6 also shows that at Which Road accuracy, all receiver combinations and both processing methods yield almost identical performance.
Figure 6A. Comparison of V2V solutions using RTK and DPOS methods.Figure 6B. Comparison of V2V solutions using RTK and DPOS methods.Figure 6C. Comparison of V2V solutions using RTK and DPOS methods.
Other Approaches
Given that carrier-phase measurements are subject to cycle slips in some road environments, we ran a test using code measurements only in relative mode, using selected data sets collected on a mountainous highway. Only common satellites were used. Given that code measurements are not affected by a loss of phase lock, such a solution is more robust, but is subject to code noise and multipath. The RMS horizontal position differences between these solutions and the reference inter-vehicle separations were 25 centimeters and 1 meter for receiver Types A and B, respectively. Both receiver types meet the Where-in-Lane requirement in this test. Type A, with its low code noise and excellent code multipath-reduction capability, has a clear advantage.
Such an approach would represent a compromise between the DPOS and RTK approaches. Its advantage over the RTK approach is a lower data transmission-rate requirement, while that over the DPOS approach is the use of common satellites only. The latter is quite significant, since low-elevation satellites contribute the most to horizontal position solutions, but their measurements are affected more by atmospheric transmission errors that are most effectively removed in differential mode on a satellite-by-satellite basis.
V2V Operation with V2I
While infrastructure support can almost always improve the performance of other V2X applications, it can pose a challenge for positioning when such coverage is not continuous. The complication arises as a result of vehicles transitioning in and out of V2I coverage areas. V2I systems are highly likely to include GNSS augmentation capability so that vehicles within a coverage area benefit from better positioning capability. However, when vehicles transition from standard (V2V) operation mode to a V2I enhanced mode, some effects in the vehicle position domain can pose potential challenges for DPOS-based V2V.
The field study included test scenarios with limited V2I coverage in different driving environments: all of those described above with the exceptions of deep urban and mountains. In deployment, the infrastructure points (IPs) would broadcast aiding information to the vehicles within their coverage area, allowing real-time calculations. In the field study, in which the role of the IP was filled by a stationary high-grade receiver with a tripod-mounted antenna, all V2I estimates of the IVV were calculated using post-processing. Further, V2I estimates of the IVV were only calculated when at least one of the vehicles was within the coverage area of the IP, here chosen to be a circle of radius 300 meters centered at the IP. This range was chosen since it is the nominal effective range of the DSRC link.
The location of the IP, that is, the phase center of the stationary antenna, was determined using commercial RTK network software with additional stations at precise locations on the rooftop of a building at the University of Calgary. The estimated accuracy of this position was 5 millimeters (1 sigma). The distances of the vehicles from the IP, used to indicate when the vehicles transitioned into and out of the IP coverage area, were determined using the GPS/INS reference trajectories. In post-processing, once a vehicle was identified as having entered the IP coverage area, commercial RTK software was used to estimate the position of the vehicle, using the IP as base and each of the test receivers on that vehicle as rovers. The IVV was then calculated using the difference of the positions of the two vehicles. Thus, the V2I estimate of the IVV was determined using what is essentially the DPOS method with stationary base RTK-indicated vehicular positions, instead of the less accurate single-point GPS position solutions. When only one vehicle was within the coverage area, single-point solutions were used for the distal vehicle, resulting in a solution called V2I-S.
Figure 7 shows two sets of CDFs generated to illustrate the V2V positioning accuracy with V2I capability. The left plot corresponds to AW–AW receiver combination, and the right plot corresponds to the BW–BW combination. Each plot includes four curves. One pair of curves shows the V2V positioning accuracy without V2I, which includes performance when using the DPOS method (green) and another when using RTK (blue). The second pair shows the accuracy of the V2I and V2I-S estimates.
The most striking observation from Figure 7 is the separation of the V2I-S case from others for both receiver combinations (purple). It shows much worse positioning accuracy compared to the other three curves. For instance, using a BW–BW pair, the system will meet the Which Lane accuracy requirement around 80 percent of the time for either DPOS or RTK V2V without V2I support. However, when V2I coverage is available to only one vehicle, the V2I-S case, the accuracy requirement is only met at 40 percent confidence.
Figure 7A. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).Figure 7B. Average relative positioning accuracy as a function of V2I positioning modes (orange V2I; green DPOS; blue RTK; purple V2I-S).
Thus, system accuracy performance degrades when vehicles are operating in DPOS mode and are transitioning in and out of the V2I zones. This is because the V2I-S estimate is the difference of an accurate position solution for the vehicle within the coverage zone, and a potentially inaccurate single-point solution for the one outside the coverage zone. The beneficial cancellation of similar errors that occurs for DPOS estimates (using similar receivers and with common satellite observations) does not occur for V2I-S.
Potential solutions to this problem include using a V2I method of IVV calculation that is not dependent on the estimated position alone (that is, use RTK or other measurement-based methods as opposed to DPOS), or using a position-mode indicator with the DPOS mode such that a DPOS-based V2V solution is only generated when both vehicles are operating in the same mode (that is, V2I). However, the latter does not provide a remedy for the complications when the two vehicles are operating in two different modes. One could also consider a variation of the latter method whereby a V2I-augmented position and a non-augmented position is maintained by each vehicle, such that one of them could be used to generated a mode-matched DPOS V2V solution for a given sender.
Recommendations
These extensive trials provided valuable data demonstrating technical challenges associated with V2X positioning.
Error characteristics and modeling in the navigation solutions in receivers A and B are type-dependent, and they may not be compatible when a receiver mix is used with the DPOS mode. This is very likely to be the case for many other commercial receivers. Therefore, it is important to develop receiver hardware and software minimum-performance standards that define acceptable performance for measurement quality, satellite tracking and selection criteria, reliability estimates, navigation-solution parameters, and other such indicators.
Findings with RTK confirm the fact that use of measurement data eliminates some of the limitations associated with the DPOS method. While RTK is the most demanding raw data-based method in terms of processin
g requirements and OTA data needs, the study also conducted limited investigation on other methods that use raw code data and are less resource-intensive, and at the same time better performing than DPOS. Such an approach would represent a compromise between the DPOS and RTK approaches.
An important conclusion based on this data is that more than 98 percent of the individual vehicle-level service outages in the entire study lasted less than 30 seconds using any one of the receiver types. For the deep urban environment, 93 percent of the outages were less than 30 seconds. These statistics are useful for future research on suitable GNSS augmentation methods.
System accuracy performance degrades when vehicles operate in DPOS mode and transition in and out of the V2I zones. Potential solutions should be incorporated into the systems to take care of these limitations.
Acknowledgments
The authors thank the Crash Avoidance Metrics Partnership Vehicle Safety Communications-Applications team, in particular the Vehicle Positioning Technology Development team, for input. This work was conducted as a part of a CAMP VSC-A project under a cooperative agreement with the U.S. DOT.
CHAMINDA BASNYAKE is a senior research engineer at General Motors Global Research and Development and GNSS technology expert for GM OnStar. He leads GNSS-based vehicle navigation technology R&D efforts at GM and holds a Ph.D. in geomatics engineering from the University of Calgary.
TOM WILLIAMS is a postdoctoral researcher in the PLAN group in the Department of Geomatics Engineering at the University of Calgary.
PAUL ALVES is a Calgary-based geomatics consultant specializing in RTK. He obtained his doctorate from the University of Calgary.
GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.
By John Nielsen, Ali Broumandan, and Gérard Lachapelle
Ubiquitous adoption of and reliance upon GPS makes national and commercial infrastructures increasingly vulnerable to attack by criminals, terrorists, or hackers. Some GNSS signals such as GPS P(Y) and M-code, GLONASS P-code, and Galileo’s Public Regulated Service have been encrypted to deny unauthorized access; however, the security threat of corruption of civilian GNSS signals increases constantly and remains an unsolved problem. We present here an efficient approach for the detection and mitigation of spoofed GNSS signals, as a proposed countermeasure to add to the existing system.
Current methods to protect GPS civilian receivers from spoofing signals are based on the cross-check with available internal/external information such as predictable characteristics of the navigation data bits or correlation with ancillary inertial-based sensors; alternately, a joint process of signals received at two separate locations based on processing the P(Y)-code.
The authentic GNSS signal sourced from a satellite space vehicle (SV) is very weak at the receiver’s location and is therefore vulnerable to hostile jamming based on narrowband noise radiation at a modest power level. As the GNSS frequency band is known to the jammer, the effectiveness of the latter is easily optimized by confining radiation to within the GNSS signal band. The jammed GNSS receiver is denied position or time estimates which can be critical to the mission. While noise jamming of the GNSS receiver is a threat, the user is easily aware of its existence and characteristics. The worst case is that GNSS-based navigation is denied.
A more significant jamming threat currently emerging is that of the spoofing jammer where bogus signals are transmitted from the jammer that emulate authentic GNSS signals. This is done with multiple SV signals in a coordinated fashion to synthesize a plausible navigation solution to the GNSS receiver. There are several means of detecting such spoofing jammers, such as amplitude discrimination, time-of-arrival discrimination, consistency of navigation inertial measurement unit (IMU) cross-check, polarization discrimination, angle-of-arrival (AOA) discrimination, and cryptographic authentication.
Among these authentication approaches, the AOA discriminator and spatial processing have been addressed and utilized widely to recognize and mitigate hostile attacks. We focus here on the antenna-array processing problem in the context of spoofing detection, with considerations to the pros and cons of the AOA discriminator for handheld GNSS receivers.
An exploitable weakness of the spoofing jammer is that for practical deployment reasons, the spoofing signals generally come from a common transmitter source. Hence, a single jamming antenna sources the spoofing signals simultaneously. This results in a means of possible discrimination between the real and bogus GNSS signals, as the authentic GNSS signals will emanate from known bearings distributed across the hemisphere.
Furthermore, the bearing of the jammer as seen from the GNSS receiver will be different than the bearing to any of the tracked GNSS satellites or space vehicles (SV). This immediately sets up some opportunities for the receiver to reject the spoofing jamming signals. Processing can be built into the receiver that estimates the bearing of each SV signal. Note that the relative bearings of the GNSS signals are sufficient in this case, as the bogus signals will all have a common bearing while the authentic GNSS signals will always be at different bearings.
If the receiver comprises multiple antennas that have an unobstructed line of sight (LOS) to the SVs, then there are possibilities of spoofing detection based on the common bearing of the received GNSS signals and eliminating all the jammer signals simultaneously by appropriate combining of the receiver antennas to form a pattern null coincident with the jammer bearing.
Unfortunately, the AOA discrimination will not be an option if the jammer signal or authentic signals are subjected to spatial multipath fading. In this case, the jammer and individual SV signals will come in from several random bearings simultaneously. Furthermore, if the GNSS receiver is constrained by the form factor of a small handset device, an antenna array will not be an option. As the carrier wavelength of GNSS signals is on the order of 20 to 25 centimeters, at most two antennas can be considered for the handset receiver, which can be viewed as an interferometer with some ability of relative signal-bearing estimation as well as nulling at specific bearings.
However, such an antenna pair is not well represented by independent isotropic field sampling nodes, but will be significantly coupled and strongly influenced by the arbitrary orientation that the user imposes. Hence, the handset antenna is poorly suited for discrimination of the spoofing signal based on bearing. Furthermore, handheld receivers are typically used in areas of multipath or foliage attenuation, and therefore the SV signal bearing is random with significant variations.
As we discuss here, effective spoofing detection is still possible for a single antenna GNSS receiver based on the differing spatial correlation of the spoofing and authentic signals in the proximity of the receiver antenna. The basic assumption is that the antenna will be spatially moved while collecting GNSS signal snapshots. Hence, the moving antenna generates a signal snapshot output similar to that of a synthetic array (SA), which, under some additional constraints, can provide an effective means of detecting the source of the GNSS signals from a spoofing jammer or from an authentic set of SVs.
We assume here an arbitrary antenna trajectory with the spoofing and authentic signals subjected to random spatial multipath fading. The processing will be based on exploiting the difference in the spatial correlation of the spoofing and the authentic signals.
Spoofing Detection Principle
Consider a GNSS handset receiver (Figure 1) consisting of a single antenna that is spatially translated in time along an arbitrary trajectory as the signal is processed by the GNSS receiver. There are L authentic GNSS SV signals visible to the receiver, along with a jammer source that transmits spoofing replicas of the same Lauthentic signals.
FIGURE 1. GNSS receiver with a single antenna and 2L parallel despreading channels simultaneously providing channel gain estimates of L authentic and L spoofing signals as the antenna is moved along an arbitrary spatial trajectory.
It is assumed that the number of spoofed signals range from 1 to L, which are coordinated such that they correspond to a realistic navigation solution at the output of the receiver processing. The code delay and Doppler associated with the spoofing signals will typically be different than those of the authentic signal. The basic technique of coordinated spoofing jamming is to present the receiver with a set of L signals that appear to be sufficiently authentic such that the spoofing and authentic signal sets are indistinguishable. Then the spoofing signals separate slowly in terms of code delay and Doppler such that the navigation solution corresponding to the L spoofing signals will pull away from the authentic navigation solution.
The focus herein is on methods where the authenticity of the L tracked GNSS signals can be tested directly by the standalone receiver and then selected for the navigation processing. This is in contrast with other methods where the received signals are transmitted back to a communication command center for verification of authenticity. The consideration here is on the binary detection problem of assessing if each of the 2L potential signals is authenti
c or generated by a spoofing source. This decision is based on observations of the potential 2L GNSS signals as the antenna is spatially moved through the trajectory.
The complex baseband signal at the output of the antenna, denoted by r(t), can be expressed as
where i is the GNSS signal index, the superscripts A and J indicate authentic and jamming signals respectively, p(t) shows the physical position vector of the moving antenna phase center relative to a stationary spatial coordinate system, ΛAi(p(t),t) and ΛJi(p(t),t) give the channel gain for the authentic and the spoofing signals of the ith SV at time t and position p, ci(t) is the PN coding modulation of ith GNSS signal, πAi and πJi are the code delay of ith PN sequence corresponding to the authentic and the spoofing sources respectively, fDiA and fDiJ are the Doppler frequency of the ith authentic and the spoofing signals and w(t) represents the complex baseband of additive noise of receiver antenna. For convenience, it is assumed that the signal index iε[1, 2,…,L] is the same for the spoofing and authentic GNSS signals. The spoofer being aware of which signals are potentially visible to the receiver will transmit up to L different spoofing signals out of this set.
Another simplification that is implied by Equation 1 is that the message coding has been ignored, which is justifiable as the GNSS signals are being tracked such that the message symbol modulation can be assumed to be removable by the receiver by some ancillary process that is not of interest in the present context. The objective of the receiver despreading operation is to isolate the channel gains ΛA(p(t),t) ΛJ(p(t),t), which are raw observables used in the subsequent detection algorithm.
It is assumed that the GNSS receiver is in a signal tracking state. Hence, it is assumed that the data coding, code phase of the spreading signal and Doppler are known inputs in the despreading operation. The two outcomes of the ith despreading channel for authentic and jamming signals are denoted as riA(t) and rkJ(t) respectively, as shown in Figure 1. This notation is used for convenience and not to imply that the receiver has knowledge of which of the pair of GNSS signals corresponds to the authentic or spoofer cases. The receiver processing will test each signal for authenticity to select the set of L signals that are passed to the navigation estimator.
The despread signals riA(t) and rkJ(t) are collected over a snapshot interval of tε[0,T]. As the notation is simplified if discrete samples are considered, this interval is divided into M subintervals each of duration ΔT such that the mth subinterval extends over the interval of [(m−1)ΔT,mΔT]for mε[1,,2,…,M]. The collection of signal over the first and mth subintervals is illustrated in Figure 2. ΔT is considered to be sufficiently small such that ΛAi(p(t),t) or ΛJk(p(t),t) is approximately constant over this interval leading a set of M discrete samples for each despreading output. From this the vectors form of channel gain sample and outputs of despreaders can be defined by
where ΛAi(p(mΔT),mΔT) and ΛJi(p(mΔT),mΔT) are the mth time sample of the ith despreader channel for the authentic and jamming GNSS signals.
Figure 2. Spatial sampling of the antenna trajectory into M subinterval segments.
Pairwise Correlation
The central tenet of the spoofing detection is that the array gain vector denoted here as the array manifold vector for the jammer signals ΛJ will be the same for all of the L spoofing signals while the array manifold vector for the authentic signals ΛA will be different for each of the L authentic signals. If the random antenna trajectory is of sufficient length, then the authentic signal array manifold vectors will be uncorrelated. On the other hand, as the jammer signals emerge from the same source they will all have the same array manifold vector regardless of the random antenna trajectory and also regardless of the spatial fading condition. This would indicate that a method of detecting that a spoofer is present to form the Mx2L matrix of all of the despreader output vectors denoted as r and given as
where it is assumed that M≥2L.
Basically what can be assumed is that, if there is a spoofer from a common source that transmits more than one GNSS signal simultaneously, there will be some residual spatial correlation of the observables of ΛJi with other despreader outputs of the receiver. Therefore, if operations of pairwise correlations of all of the 2L despreader outputs result in high correlation, there is a likelihood of the existence of spoofing signals. These pairwise correlations can also be used to distinguish spoofing from authentic signals. Note that even during the time when the spoofing and authentic signals have the same Doppler and code offset, the superposition manifold vector of ΛAi and ΛJi will be correlated with other spoofing manifold vectors. The pairwise correlation of the various spoofing signals can be quantified based on the standard numerical estimate of the correlation coefficient given as
where ri is the ith column vector of r defined in Equation 3, and the superscript H denotes the complex conjugate operator.
Toward Spoofing Detection
Figure 3 shows the spoofing attack detection and mitigation methodology:
The receiver starts with the acquisition process of a given GNSS code. If, for each PN sequence, there is more than one strong peak above the acquisition threshold, the system goes to an alert state and declares a potential spoofing attack. Then the receiver starts parallel tracking on each individual signal.
The outputs of the tracking pass to the discriminator to measure the correlation coefficient ρ among different PN sequences. As shown in Figure 3, if ρ is greater than a predefined threshold ϒ, the receiver goes to defensive mode. As the spoofer attempts to pull the tracking point off the authentic signals, the spoofer and authentic signals for a period of time will have approximately the same code offset and Doppler frequency. Hence, it may not be possib
le to detect more than one peak in the acquisition mode. However, after a while the spoofer tries to pull tracking mode off.
The outputs of the parallel tracking can be divided into two groups: the J group is the data set that is highly correlated, and the A group is the set that is uncorrelated. It is necessary that the receiver antenna trajectory be of sufficient length (a few tens of the carrier wavelengths) such that M is moderately large to provide a reasonable estimate of the pairwise correlation.
The A group will be constrained in size based on the number of observable satellites. Usually this is known, and L can be set. The receiver has control over this by setting the bank of despreaders. If an SV signal is known to be unobtainable due to its position in the sky, it is eliminated by the receiver. Hence the A group can be assumed to be constrained in size to L. There is the possibility that a spoofer will generate a signal that is clear, while the SV signal is obscured by shadowing obstacles. Hence a spoofing signal can inadvertently be placed in the A group. However, as this signal will be correlated with other signals in the J group, it can be transferred from the A to the J group.
When the spoofing navigation solution pulls sufficiently away from the authentic solution, then the navigation solution can create two solutions, one corresponding to the authentic signals and the other corresponding to the spoofing signals. At this stage, the despreading code delay and Doppler will change such that the authentic and spoofing signals (corresponding to the same GNSS signal) will appear to be orthogonal to each other.
Proper placement of the members in the J and A groups can be reassessed as the set of members in the A group should provide the minimum navigation solution variance. Hence, in general there will be a spoofing and authentic signal that corresponds to the GNSS signal of index i. If the spoofing signal in group J appears to have marginal correlation with its peer in group A and, when interchanged with its corresponding signal in group A, the latter generates a lower solution variance, then the exchange is confirmed.
Figure 3. Spoofing detection and mitigation methodology.
Experimental Measurements
We used two data collection scenarios in experiments of spoofing detection, based on utilizing a single antenna that is spatially translated, to demonstrate the practicality of spoofing-signal detection based on spatial signal correlation discrimination. In the first scenario, the spoofing measurements were conducted inside a modern three-story commercial building. The spoofing signals were generated by a hardware simulator (HWS) and radiated for a few minutes indoors, using a directional antenna pointing downward to affect only a small area of the building. The intention was to generate NLOS propagation conditions with significant multipath.
The second data collection scenario was based on measuring authentic GPS L1 C/A signals under open-sky conditions, in which case the authentic GPS signals are temporally highly correlated. At the particular instance of the spoofing and the authentic GPS signal measurement scenarios, the SVs were distributed as shown in Figure 4. The GPS receiver in both scenarios consisted of an active patch right-hand circular polarized (RHCP) antenna and a down-conversion channelizer receiver that sampled the raw complex baseband signal. The total data record was subsequently processed and consisted in acquiring the correlation peaks based on 20-millisecond coherent integration of the spoofing signals and in extracting the channel gains L as a function of time.
Figure 4. Skyplots of available satellites: a) spoofing signals from Spirent generator, b) authentic signals from rooftop antenna.
Figure 5 shows a plot of the samples of the magnitude of despreader outputs for the various SV signals generated by the spoofing jammer and authentic signals. The signal magnitudes in the spoofing case are obviously highly correlated as expected, since the jammer signals are all emanating from a common antenna. Also, the SNRs are moderately high such that the decorrelation due to the channel noise is not significant.
The pairwise correlation coefficient using Equation 4 are calculated for the measurement results represented in Figure 5 and tabulated in Table 1 and Table 2 for the spoofing and the authentic cases respectively. As evident, and expected, the correlations for the spoofing case are all very high. This is anticipated, as the spoofing signals all occupy the same frequency band with exception of small incidental shifts due to SV Doppler.
Figure 5. Normalized amplitude value of the signal amplitude for different PRNs: a) generated from the same antenna, b) Authentic GPS signals.TABLE 1. Correlation coefficient deter- mined for the set of spoofing signals.TABLE 2. Correlation coefficient deter- mined for the set of authentic signals.
Conclusions
Spoofing signals generated from a common source can be effectively detected using a synthetic array antenna. The key differentiating attribute exploited is that the spoofing signals emanating from a single source are spatially correlated while the authentic signals are not. The method works regardless of the severity of multipath that the spoofing or authentic signals may be subjected to. The receiver antenna trajectory can be random and does not have to be jointly estimated as part of the overall spoofing detection.
A patent is pending on this work.
Manufacturers
The experimental set-up used a Spirent GSS7700 simulator, National Instruments receiver (NI PXI-5600 down converter, and NI PXI-5142 digitizer modules), TECOM directional helical antennas as the transmitter antenna, and NovAtel GPS-701-GG as the receiver antenna.
JOHN NIELSEN is an associate professor at the University of Calgary.
ALI BROUMANDAN is a senior research associate in the Position Location And Navigation (PLAN) group at the University of Calgary. He obtained a Ph.D. in Geomatics Engineering from the University of Calgary in 2009.
GERARD LACHAPELLE holds an iCORE/CRC Chair in Wireless Location and heads the PLAN Group in the Department of Geomatics Engineering at the University of Calgary.
![FIGURE 6. Gain pattern of the patch antenna as measured by the measured C/N0 at all elevation angles as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º]. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle](https://stage.globalpositioningnews.com/wp-content/uploads/2012/02/Inn-Fig6.jpg)
![FIGURE 9. Gain pattern of the quadrifilar antenna as measured by the C/N0 of all measurements as a function of antenna distance from body. Elevation angles [0º, 90º] have azimuths [180º, 360º], while elevation angles [90º, 180º] have azimuths [0º, 180º]. Jared B. Bancroft, Valérie Renaudin, Aiden Morrison, and Gérard Lachapelle](https://stage.globalpositioningnews.com/wp-content/uploads/2012/02/Inn-Fig9.jpg)
