European Commission Vice President Antonio Tajani announced in London that the consortium led by OHB System AG and Surrey Satellite Technology Ltd. (SSTL) will build a further eight satellites for the European Union’s Galileo satellite navigation program under the supervision of the European Space Agency.
The new contract will see SSTL continuing its role as payload prime, assembling, integrating and testing the navigation payloads in the UK, whilst OHB System, as the prime contractor, builds the eight satellite platforms and executes the final integration of all the satellites in Germany. The SSTL-OHB partnership is already building fourteen satellites for the Galileo program and will draw on its heritage and experience to produce the additional satellites to demanding schedules.
Matt Perkins, SSTL Group CEO commented “SSTL has played a key role in the development of the Galileo program for nine years and we have the commitment, experience and track record to deliver this substantial contract. We are delighted to have been selected with our partner, OHB, to continue to play our part in building Europe’s operational navigation system.”
SSTL is assembling the Galileo program payloads at its recently opened purpose-built Kepler technical facility in Guildford, UK. Under the contract, SSTL is fully responsible for the construction and test of the navigation payloads. SSTL will manufacture the electrical harnesses and the electronics to interface the navigation payload with the satellite platform. The remaining payload equipment will be externally procured by SSTL from European and other suppliers. SSTL's payload solution is based on European-sourced atomic clocks, navigation signal generators, high power travelling wave tube amplifiers and antennas and will provide all of Galileo’s services.
Galileo is Europe’s own Global Navigation Satellite System (GNSS), providing real-time positioning, navigation and timing services with unrivalled accuracy and integrity. It will be interoperable with the American GPS system and Russia’s GLONASS system.
The Full Operational Capability phase of the Galileo program is managed and fully funded by the European Union. The Commission and ESA have signed a delegation agreement by which ESA acts as design and procurement agent on behalf of the Commission. The views expressed in this Press Release can in no way be taken to reflect the official opinion of the European Union and/or ESA. “Galileo” is a trademark subject to OHIM application number 002742237 by EU and ESA.
By Jordan Britt, David Bevly, and Christopher Rose
Nearly half of all highway fatalities occur from unintended lane departures, which comprise approximately 20,000 deaths annually in the United States. Studies have shown great promise in reducing unintended lane departures by alerting the driver when they are drifting out of the lane. At the core of these systems is a lane detection method typically based around the use of a vision sensor, such as a lidar (light detection and ranging) or a camera, which attempts to detect the lane markings and determine the position of the vehicle in the lane. Lidar-based lane detection attempts to detect the lane markings based on an increase in reflectivity of the lane markings when compared to the road surface reflectivity. Cameras, however, attempt to detect lane markings by detecting the edges of the lane markings in the image. This project seeks to compare two different lane detection techniques-one using a lidar and the other using a camera. Specifically, this project will analyze the two sensors’ ability to detect lane markings in varying weather scenarios, assess which sensor is best suited for lane detection, and determine scenarios where a camera or a lidar is better suited so that some optimal blending of the two sensors can improve the estimate of the position of the vehicle over a single sensor.
Lidar-based lane detection
The specific lidar-based lane detection algorithm for this project is based on fitting an ideal lane model to actual road data, where the ideal lane model is updated with each lidar scan to reflect the current road conditions. Ideally, a lane takes on a profile similar to the 100-averaged lidar reflectivity scans seen in Figure 1 with the corresponding segment. Figure 1. Lidar reflectivity scan with corresponding lane markings.
Note that this profile has a relatively constant area bordered by peaks in the data, where the peaks represent the lane markings and the constant area represents the surface of the road. An ideal lane model is generated with each lidar scan to mimic this averaged data, where averaging the reflectivity directly in front of the vehicle generates the constant portion and increasing the average road surface reflectivity by 75 percent mimics the lane markings. This model is then stretched over a range of some minimum expected lane width to some maximum expected lane width, and the minimum RMSE between the ideal lane and the lidar data is assumed to be the area where the lane resides. For additional information on this method, see Britt, Rose & Levy, September 2011.
Camera-based lane detection
The camera-based method for this project was built in-house and uses line extraction techniques from the image to detect lane markings and calculate a lateral distance from a second-order polynomial model for the lane marking in image space. A threshold is chosen from the histogram of the image to compensate for differences in lighting, weather, or other non-ideal scenarios for extracting the lane markings. The thresholding operation converts the image into a binary image, which is followed by Canny edge detection. The Hough transform is then used to extract the lines from the image, fill in holes in the lane marking edges, and exclude erroneous edges. Using the slope of the lines, the lines are divided into left or right lane markings. Two criteria based on the assumption that the lane markings do not move significantly within the image from frame to frame are used to further exclude non-lane marking lines in the image. The first test checks that the slope of the line is within a threshold of the slope of the near region of the last frame’s second-order polynomial model. The second test uses boundary lines from the last frame’s second-order polynomial to exclude lines that are not near the current estimate of the polynomial. second-order polynomial interpolation is used on the selected lines’ midpoint and endpoints to determine the coefficients of the polynomial model, and a Kalman filter is used to filter the model to decrease the effect of erroneous polynomial coefficient estimates. Finally, the lateral distance is calculated using the polynomial model on the lowest measurable row of the image (for greater resolution) and a real-distance-to-pixel factor. For more information on this camera-based method, see Britt, et al.
Figure 2. Camera-based lane detection (green-detected lanes,blue-extracted lane lines, red-rejected lines).
Testing
Testing was performed at the NCAT (National Center for Asphalt Technology) in Opelika, Alabama, as seen in Figure 3. This test track is very representative of highway driving and consists of two lanes bordered by solid lane markings and divided by dashed lane markings. The 1.7-mile track is divided into 200-foot segments of differing types of asphalt with some areas of missing lane markings and other areas where the lanes are additionally divided by patches of different types and colors of asphalt.
Figure 3. NCAT Test Facility in Opelika, Alabama.
A precision survey of each lane marking of the test track as well as precise vehicle positions using RTK GPS were used in order to have a highly accurate measurement of the ability of the lidar and camera to determine the position of the vehicle in the lane. Testing occurred only on the straights, and the performance was analyzed on the ability of the lidar and camera to determine the position of the lane using metrics of mean absolute error (MAE), mean square error (MSE), standard deviation of error (σerror), and detection rate. The specific scenarios analyzed included varying speeds, varying lighting conditions (noon and dusk/ dawn), rain, and oncoming traffic. Table 1 summarizes the results for these scenarios. For additional results, please see [8].
Scenario
MAE(m)
MSE(m)
σerror (m)
%Det
Lidar
Noon Weaving
0.1818
0.1108
0.3076
98
Camera
Noon Weaving
0.1077
0.0511
0.2246
80
Lidar
Dusk 45mph
0.0967
0.0176
0.1245
100
Camera
Dusk 45mph
0.2021
0.0592
0.2433
57
Lidar
Medium Rain
0.1046
0.0177
0.1314
65
Camera
Medium Rain
0.0885
0.0101
0.0635
91
Lidar
Low Beam, Night
0.0966
0.0159
0.1215
99
Camera
Low Beam, Night
0.1182
0.0185
0.0762
84
Table 1. Lidar and camera results for various environments.
Additional testing on the effects of oncoming traffic at night was examined by parking a vehicle on the test track at a known location with the headlights on. Figure 4 shows the lateral error with respect to closing distance where a positive closing distance indicates driving at the parked vehicle, and a negative closing distance indicates driving away from the vehicle. Note that the camera does not report a solution at -200 m, which is due to track conditions and not the parked vehicle.
Figure 4. Error vs. Closing Distance.
Based on these findings it would appear that the camera provided slightly more accurate measurements than the lidar while having a decrease in detection rate. Additionally the camera performed well in the rain where the lidar experienced decreased detection rates.
References
Frank S. Barickman. Lane departure warning system research and test development. Transportation Research Center Inc., (07-0495), 2007.
J. Kibbel, W. Justus, and K. Furstenberg. using multilayer laserscanner. In Proc. Lane estimation and departure warning Proc. IEEE Intelligent Transportation Systems, pages 607 611, September 13 15, 2005.
P. Lindner, E. Richter, G. Wanielik, K. Takagi, and A. Isogai. Multi-channel lidar processing for lane detection and estimation. In Proc. 12th International IEEE Conference on Intelligent Transportation Systems ITSC ’09, pages 1 6, October 4 7, 2009.
K. Dietmayer, N. Kämpchen, K. Fürstenberg, J. Kibbel, W. Justus, and R. Schulz. Advanced Microsystems for Automotive Applications 2005. Heidelberg, 2005.
C. R. Jung and C. R. Kelber, “A lane departure warning system based on a linear-parabolic lane model,” in Proc. IEEE Intelligent Vehicles Symp, 2004, pp. 891–895.
C. Jung and C. Kelber, “A lane departure warning system using lateral offset with uncalibrated camera,” in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, sept. 2005, pp. 102 – 107.
A. Takahashi and Y. Ninomiya, “Model-based lane recognition,” in Proc. IEEE Intelligent Vehicles Symp., 1996, pp. 201–206.
Jordan Britt, C. Rose, & D. Bevly, “A Comparative Study of Lidar and Camera-based Lane Departure Warning Systems,” Proceedings of ION GNSS 2011, Portland, OR, September 2011.
European Commission Vice President Antonio Tajani announced in London that the consortium led by OHB System AG and Surrey Satellite Technology Ltd. (SSTL) will build a further eight satellites for the European Union’s Galileo satellite navigation program under the supervision of the European Space Agency.
The new contract will see SSTL continuing its role as payload prime, assembling, integrating and testing the navigation payloads in the UK, whilst OHB System, as the prime contractor, builds the eight satellite platforms and executes the final integration of all the satellites in Germany. The SSTL-OHB partnership is already building fourteen satellites for the Galileo program and will draw on its heritage and experience to produce the additional satellites to demanding schedules.
Matt Perkins, SSTL Group CEO commented “SSTL has played a key role in the development of the Galileo program for nine years and we have the commitment, experience and track record to deliver this substantial contract. We are delighted to have been selected with our partner, OHB, to continue to play our part in building Europe’s operational navigation system.”
SSTL is assembling the Galileo program payloads at its recently opened purpose-built Kepler technical facility in Guildford, UK. Under the contract, SSTL is fully responsible for the construction and test of the navigation payloads. SSTL will manufacture the electrical harnesses and the electronics to interface the navigation payload with the satellite platform. The remaining payload equipment will be externally procured by SSTL from European and other suppliers. SSTL’s payload solution is based on European-sourced atomic clocks, navigation signal generators, high power travelling wave tube amplifiers and antennas and will provide all of Galileo’s services.
Galileo is Europe’s own Global Navigation Satellite System (GNSS), providing real-time positioning, navigation and timing services with unrivalled accuracy and integrity. It will be interoperable with the American GPS system and Russia’s GLONASS system.
The Full Operational Capability phase of the Galileo program is managed and fully funded by the European Union. The Commission and ESA have signed a delegation agreement by which ESA acts as design and procurement agent on behalf of the Commission. The views expressed in this Press Release can in no way be taken to reflect the official opinion of the European Union and/or ESA. “Galileo” is a trademark subject to OHIM application number 002742237 by EU and ESA.
Mobility’s first phase saw fixed-line communications go mobile. The next phase saw the Internet go mobile. We now behold a paradigm shift in the third phase, where real world communication bridges to the virtual world, via richer communications on smartphones.
For device manufacturers and location-aware service and app creators, it’s no longer about creating unique standalone experiences, it’s about enhancing real-time experiences by enriching everyday consumer behavior with virtual content and relevant information to a particular place and point in time. Location is an important canvas to a series of components that will unlock the possibilities of a more fulfilling, spontaneous — and sometimes amazing — mobile experience. By bringing together the quality of positioning and maps, enabling personalization with places and recommendations, evolving the simple check-in, and enhancing the experience with augmented reality, we activate a seamless, immersive experience that adds value to consumers’ daily life adventures.
Most importantly for wireless operators, location, as a key part of context and relevance, provides a unique opportunity to create revenue.
Location Positioning and Maps
As we create advanced mobile positioning technologies, consumers increasingly become accustomed to location-aware services. Outdoor positioning was our entrée into the market, and it has becoming more and more accurate via new satellite systems in addition to GPS (GLONASS, SBAS, QZSS), use of motion sensors, assisted-GNSS enhancements, and software algorithms to enable instant time to first fix (TTFF), and seamless fixes. On the other hand, pinpointing your location indoors still presents challenges from an accuracy standpoint.
At Nokia, we support Open Mobile Alliance Secure User Plane Location (OMA SUPL, incorporating AGPS and cell-ID) standards for our devices, and enhance our proprietary Nokia Positioning Service (NPS) based on leading-edge assisted-GNSS (GPS+GLONASS) technologies. Our NPS service supports global crowd-sourced databases for cellular tower and Wi-Fi access-point location information. These provide virtually instant TTFF everywhere and enable always-on location awareness — even on devices without an integrated GPS receiver or data connectivity.
3-D Building overlay for real-world representation.Heat map. to see where the action is: concentrations of location-enabled mobile phone users that can provide data on places where others are dining, dancing, or shopping.
We’re also setting our sights on the next frontier: research concept around high accuracy indoor positioning (HAIP) technologies. Nokia’s current HAIP trial system relies on a dedicated positioning beacon, which acts as an indoor satellite when placed on the ceiling. It can accurately locate your position in a room and how far you are from your desired destination in real-time, with an accuracy of up to 30 centimeters. In this manner, we could direct a potential customer to a physical store front, and further to a specific product on the shelf inside the store.
HAIP beacon from Nokia, for high-accuracy indoor positioning.
Another example comes from Shopkick, with its own proprietary solution for indoor positioning that utilizes a similar beacon placed inside a retail store. On the device side, the ShopKick app listens via the mobile device microphone and alerts a company when a valued customer physically walks into its store. According to TechCrunch, one of Shopkick’s partner retailers “is estimating $50 million in measurable incremental revenue as a result of the Shopkick mobile app.”
The business opportunity is clear: retailers can now directly connect to the consumer for one-to-one marketing and engagement. Consumers are rewarded instantly, on the spot, and enticed to collect further rewards through loyalty programs.
Imagine enhancing this experience further with a visual representation of your position on a map in an outdoor situation, which can offer a wealth of functionality and create a 3D representation of the real world. At Nokia we are further enhancing our NavTeq maps that deliver accurate 360-degree panoramic street-level imagery, 3D building overlays and a point-of-interest (referred to as a place in this article) interface as individual layers. The map data collection provides individual high-density content layers that enable more fluid animation and 3D mesh building overlays. Users can highlight and select buildings and places to interact with in 3D within their surroundings. This merges the real and virtual world, allowing physical and digital objects to co-exist and interact in real time. Imagine the endless opportunities: zoom in on a 3D map of a restaurant storefront, click the menu on the window to see the special of the day, or receive a discounted offer based on something you have liked in the past.
Places and Recommendations
The way we interact with our mobile device is evolving to mimic the way we exist in the real world. When we refer to a place or to a location, for example, we don’t talk in terms of coordinates or an address, rather we say “the Starbucks around the corner from MOMA.” In building devices and applications, we build the place with the foundation of core data (name, address, longitude and latitude, contact details) and layer on top of that an ever-expanding amount of rich data that comprises ratings and reviews, hours of operation, wheelchair access and spatial data extended to entrances, and more. Thus, we begin to layer in context and we no longer need to know the Boolean constructs that we learned in Web 1.0 to talk to a search engine and find exactly what we want.
Managing this rich, evolving set of place data in a relevant manner will increase in importance. It will also open the door to getting recommendations outside of your normal social community. For example, heat maps that allow you to instantly see where the action is in cities around the world, quickly sharing insight into where locals eat, dance, and shop. Check out examples from Nokia (maps.nokia.com) and mobile apps like AroundMe or Foursquare Radar. Providing locally relevant content to end users also extends the opportunity to connect local merchants to their specific target audience or entice new ones.
JiWire reported in August that “53 percent of the on-the-go U.S. audience revealed they are willing to share their location to receive more relevant content. Mobile consumers under the age of 34 are more eager to share, with 60 percent offering their location for better information.” Focusing on the qualifier, “offering their location for better information,” is where places and recommendations become a powerful medium, and advertisements and offers become another valuable piece of the rich data set offered via your mobile device.
Consider a restaurant search that returns a result for a Chinese restaurant your friend has rated 5 stars for its Mongolian beef, which in the past, you have indicated you liked. As part of the information presented, you see a 15-percent off promotion when you view the menu prices. Or perhaps you’ve searched for a children’s museum, and navigation finds the destination and starts directing you from your current location. Upon arrival, you might receive an offer for discounted membership. As more consumers gravitate towards location-based or location-incorporating services on their smartphones, there’s a great opportunity for developers and business owners to integrate place and recommendation experiences.
Consumer Engagement
Utilizing positioning, maps, places and recommendations are the building blocks on which you can create contextually relevant experiences that consumers will find engaging and sticky and which can open the door to business opportunities. Research shows that consumers are willing to check-in to a location, either by text messaging or by using a mobile application on a smartphone; the application will use the phone’s GPS to find the current location. Many social networking services, such as Foursquare, Google+, Facebook, and Gowalla allow users to check in to a physical place and share their location with their friends. Comscore reported that “16.7 million U.S. mobile subscribers used location-based check-in services on their phones in March 2011, representing 7.1 percent of the entire mobile population.”
I still believe check-in remains a niche as it’s not a natural human behavior but is a good starting point for interacting with a location. Check-in needs to be bundled with offer redemption to encourage people to check-in. Also, check-in data can add a new layer of behavior that may not be reflected in recent purchases. For example, a check-in at a gym adds valuable lifestyle information about a consumer, which can aid in ad-targeting efforts.
Now more than ever, as we explore and engage with the world around us, we want to experience amazing everyday adventures. We can enhance this adventure further by augmenting the rich content associated with places in a visual representation that can be consumed through your mobile device in the real world. Imagine you’re in Times Square in Manhattan and you open an augmented-reality experience like Nokia City Lens or Yelp’s Monocle and start panning around you. Icons might pop up to indicate you have a loyalty card for a particular chain of coffee shop; a consumer electronics store has your favorite Wii game on sale; a good friend just gave a nearby restaurant a 5-star review. Perhaps you’ll even find you can get home in less than half an hour if you take a new suggested route that accounts for traffic that’s moving a little slower than usual on your typical drive.
The Opportunity Ahead
In this third phase of mobility, our mobile devices will be a bridge to enriching our lives with virtual content, as long as it is relevant and engaging. Location is a catalyst to enhance virtual interaction with real-world places, enticing people to visit such real-world places. For developers and marketers, business opportunities lie in using highly accurate positioning to drive consumers into storefronts and directly to the products they want; in enabling highly personalized experiences with places that present the right offer at the right time to the right consumer; and in elevating the check-in to engage and reward the consumer. Context, relevance, and consumer engagement will all provide unique monetization opportunities as location technology continues to evolve.
Christopher Peralta is head of location and advertising services for Nokia in North America, responsible for mobile navigation and location-aware services that connect users to locally and socially relevant personalized content and experiences.
Ashton Carter, U.S. deputy secretary for Defense, and John Porcari, deputy secretary for Transportation, have written an official letter to the assistant secretary of Commerce stating that “there appear to be no practical solutions or mitigations that would permit the LightSquared broadband service.” Carter and Porcari are co-chairs of the National Executive Committee for Space-Based Positioning, Navigation, and Timing. This represents the strongest intra-government statement to date on the issue.
Their letter further states that “both LightSquared’s original and modified plans for its proposed mobile network would cause harmul interference to many GPS receivers. Additionally, an analysis by the Federal Aviation Administration has concluded that the LightSquared proposals are not compatible with several GPS-dependent aircraft safety-of-flight systems.”
“No additional testing is warranted at this time,” the authors conclude.
They further propose to “draft new GPS spectrum interference standards that will help inform future proposals for non-space, commercial uses in the bands adjacent to the GPS signals.”
No response has emerged from either the Federal Communications Commission or the National Telecommunications and Information Administration, the two bodies charged with making a determination on the issue. But the letter appears to signal a coming end to a conflict that has occupied many, and tied up many resources and consumed many millions of dollars, for the past year.
One source commented off the record that “Our hope is this will be the end of the matter, and the FCC will withdrawal its initial approval and inform LSQ they must seek the 500 MHz in a different portion of the spectrum.”
Second Galileo IOV Satellite Transmits
On January 17, the E1 signal of the Galileo Flight Model 2 satellite (FM2, also known as GSAT0102) was successfully acquired and tracked by the researchers of the Navigation, Signal Analysis and Simulation (NavSAS) group at Politecnico di Torino / Istituto Superiore Mario Boella. The signal was received with a non-directive GNSS antenna, a commercial narrowband E1 RF front-end, and the N-GENE software receiver developed by the NavSAS lab.
Other research facilities and advanced GNSS companies around the world have also reported reception of a signal from this, the second in-orbit validation Galileo satellite, launched on October 21, 2011. The first IOV satellite, Galileo-ProtoFlight Model (PFM) began broadcasting in December.
FM2 currently transmits a Galileo Open Service signal on the E1 band using the Code Number 12 of the Galileo Interface Control Document (ICD). Acquisition and tracking results are reported in Figures 1, 2, and 3. The signal was received with a C/N0 of approximately 46.4 dBHz and a Doppler frequency shift equal to –2595 Hz.
Both Galileo craft were in view on January 17. Figure 4 shows both the estimated Doppler and C/N0 profiles obtained from multiple measurements performed on the same time interval.
As a final step, the demodulation of the E1b data channel has also been performed, checking the navigation messages for both the satellites. It has been noticed that, at the moment, the navigation messages present only two types of page: reserved (word type field with value 63) and type 0 (spare). Type 0 words have valid Week Number and Time Of Week fields. On the other hand, both the satellites broadcast a valid secondary code on their E1c pilot channels, compliant with the Galileo ICD.
— Fabio Dovis
FIGURE 1. Search space of the successful acquisition of the Galileo FM2 satellite (PRN 12).
FIGURE 2. Peak obtained acquiring the Galileo FM2 satellite.
FIGURE 3. Estimated C/N0 and correlation values obtained tracking the PRN 12.
FIGURE 4. Estimated Doppler and C/N0 profiles along multiple measurements performed on January 17.
More GPS III Birds, Launch, Checkout Awarded
The U.S. Air Force awarded Lockheed Martin a $238 million contract for production of the third and fourth satellites in the next-generation GPS III constellation.
In May 2008, the Air Force awarded Lockheed Martin an initial contract to design, develop and build the first two GPS III satellites. The contract also includes options for up to 10 additional spacecraft. With the most recent award, the GPS III team is now on contract to deliver four GPS III space vehicles, with the first launch scheduled in 2014. The Air Force has plans to build up to 32 GPS III satellites.
The Air Force also signed a $21.5 million contract with Lockheed Martin to provide a launch and checkout capability (LCC) to command and control all GPS III satellites from launch through early on-orbit testing.
The LCC will be integrated into the Raytheon-developed Next Generation Operational Control System (OCX). It includes trained satellite operators and engineering solutions in partnership with OCX to support launch, early orbit operations, and checkout of all GPS III satellites before the spacecraft are turned over to Air Force Space Command for operations.
“Achieving initial launch capability in 2014 is critical to introducing new GPS capabilities on time and will enable the GPS III program to continue its production pace, maximize efficiencies and reduce long term costs for the GPS enterprise as a whole,” said Col. Bernard Gruber, director of the GPS Directorate. “LCC will ensure we can launch in 2014, effectively closing the time gap between GPS III and the Next Generation Operational Control System.”
Lockheed Martin is the GPS III prime contractor with teammates ITT Exelis, General Dynamics, Infinity Systems Engineering, Honeywell, ATK, and other subcontractors.
Increase Proposed for GLONASS
A December 27 meeting in Moscow heard a proposal to expand the GLONASS constellation to 30 satellites and six orbital planes, among five other modernization options. The Presidium of the TsNIImash Council (Central Research Institute of Machine Building) is the arm of Roscosmos, the Russian federal space agency, responsibale for civil aspects of GLONASS.
The other options include adding one more satellite to each of the existing three planes, but that would involve rephasing almost all of the operating satellites, which could cause problems. Adding three new planes to the constellation, each with two satellites, is the leading option, and will be considered in detail over the next few months.
It is not clear how the present GLONASS frequency-division multiple-access (FDMA) channel spectrum could handle 30 satellites. It appears that the current arrangement can only handle a maximum of 28 satellites. The concept would need support from the Russian Defense Ministry among others to go ahead.
Incomplete Compass ICD Released
China announced the official start of Compass operational positioning, navigation, and timing services to China and surrounding areas and released a test version of an interface control document (ICD) on December 27. The ICD is available in both Chinese and English in PDF format from the system’s website, www.beidou.gov.cn.
The nine-page test ICD is incomplete. It only describes the basics of the coordinate and time systems and the basic characteristics of the open service B1 signal transmitted as the in-phase component on the 1561.098 MHz carrier frequency, including the ranging codes assigned to different satellites. There is no discussion of the details of the navigation message or associated algorithms.
A spokesperson stated that the test version is being released to stimulate research and development work and promote applications as soon as possible, and that some aspects of the transmitted signals are not yet finalized or “cured” and that is why they are not discussed in the test ICD.
Leap Second
The International Earth Rotation and Reference Systems Service (IERS) announced that a positive leap second will be introduced into Coordinated Universal Time (UTC) at the end of June 2012. UTC will be retarded by 1.0 second so that the sequence of dates of the UTC markers will be:
2012 June 30 23h 59m 59s
2012 June 30 23h 59m 60s
2012 July 01 0h 0m 0s
UTC and all time scales based on UTC will be affected by this adjustment. However, GPS will not be adjusted physically. For GPS, the leap second correction contained within the UTC data of subframe 4, page 18 of the navigation message transmitted by satellites will change.
Before the leap second: GPS-UTC = +15s (that is, GPS is ahead of UTC by 15 seconds).
After the leap second: GPS-UTC = +16s (GPS will be ahead by 16 seconds).
Meanwhile, the International Telecommunication Union postponed until 2015 a vote on a proposal to do away with leap seconds completely.
Small ceramic patch elements offer nearly perfect single-frequency receive characteristics and have become the standard for GPS L1 antennas. However, the new generation of GNSS receivers now being introduced track many satellites in multiple constellations. Are these narrow-band devices up to the task for wider bandwidths?
L1 Compass and GLONASS navigation signals are broadcast on frequencies close to GPS L1, but the offset exceeds the circular-response bandwidth of small patch antennas. This article discusses the nature of the defects to be expected with the use of small patches over the broader bandwidths required, and contrasts this with the higher performance of dual-feed patch antennas.
It is very difficult to evaluate the relative merits of GNSS antennas without very specialized equipment and resources. An accurate method for comparative evaluation of competing antennas is described that makes use of the C/N0 values reported by GNSS receivers.
A particular challenge facing GNSS is the threat posed by encroaching interfering signals; the LightSquared terrestrial segment signals often being quoted. Relatively simple measures are described to make GNSS antennas immune and the small resulting hit to antenna performance is quantified.
Circularly-Polarized Carrier Signals
The civilian signals transmitted from GNSS satellites are right hand circularly polarized (RHCP). This allows for arbitrary orientation of a receiving patch antenna (orthogonal to the direction of propagation) and, with a good co-polarized antenna, has the added benefit of cross polarization rejection.
For conceptualization, circularly polarized (CP) signals can be thought of as comprised of two orthogonal, linearly polarized signals offset in phase by 90 degrees, as shown in fig 1 below. With one feed defined as I (in-phase), and the other Q (quadrature), the response of the antenna will either be LHCP or RHCP depending upon the polarity of the Q signal phase relative to that of the I signal.
If a CP signal is reflected from a metallic surface (such as metalized glass), the reflected signal becomes cross-polarized, so that a reflected RHCP signal becomes LHCP, and vice-versa. Unlike the linearly polarized (LP) case, a good CP receiving antenna will reject cross-polarized signals resulting from a single reflection. In this respect, reception of CP signals by a CP antenna is considerably improved relatively to linearly polarized signals.
FIGURE 1. Graphic representation of circular polarization (from Innovation column, July 1998 GPS World).
Frequency Plans
At this time, four global navigation satellite systems (GNSS) are either in service or expected to achieve full operational capability within the next 2–3 years: GPS, of course, GLONASS, also now fully deployed, Galileo, and Compass, expected to be deployed over the next two years.
Thus the systems and signals to be considered are:
GPS-L1 at 1575.42 MHz;
GLONASS L1, specified at 1602MHz (+6, –7) × Fs, where Fs is 0.5625 MHz;
Compass at 1561 MHz;
Galileo L1 as a transparent overlay on the GPS system at 1575.42 MHz.
It has emerged that considerable accuracy and availability benefits derive from tracking a larger number of satellites from multiple constellations. Notably, STMicroelectronics has produced an excellent animation of the GPS and GLONASS constellations that shows the theoretical improvement in accuracy and fix availability that derive from simultaneously tracking GPS and GLONASS satellites in Milan, For a really interesting comparison check out www.youtube.com/watch?v=0FlXRzwaOvM.
Most GNSS chip manufacturers now have multi-constellational GNSS receiver chips or multi-chip modules at various stages of development. It is awe-inspiring that the navigational and tracking devices in our cars and trucks will in the very near future concurrently track many satellites from several GNSS constellations. Garmin etrex 10/20/30 handhelds now have GLONASS as well as GPS capability.
Small single-feed patch antennas have good CP characteristics over a bandwidth up to about 16 MHz. This format is cheap to build and provides almost ideal GPS L1 characteristics.
Multi-constellation receivers such as GPS/GLONASS require antennas with an operational bandwidth of up to 32 MHz, and up to 49 MHz to also cover Compass.
Patch Antenna Overview
The familiar patch element is a small square ceramic substrate, fully metalized on one side, acting as a ground plane, and on the other, a metalized square patch. This structure constitutes two orthogonal high-Q resonant cavities, one along each major axis. An incident circular electromagnetic wave induces a ground current and an induced voltage (emf) between the patch edge and ground plane so that at resonance, the cavity is coupled to free space by these fringing fields.
A typical low-cost GPS L1 patch is a 25 × 25 × 4 mm block of ceramic (or smaller) with a single-feed pin. Patches as small as 12 mm square can be fabricated on high-dielectric constant substrates, but at the cost of lower gain and bandwidth. The two axes are coupled either by chamfered patch corners or by offset tuning plus diagonal feed pin positions (Figure 2).
An alternate form of patch antenna has independent feeds for each axis. The feeds are combined in a network that fully isolates the two feeds. Dual-feed antennas can provide nearly ideal characteristics but are inherently more expensive to build. See Figure 3.
FIGURE 3. Dual-feed patch (left) and feed combiner (right).
Basic Performance Parameters
The factors that have a direct bearing on patch performance are:
Gain and radiation pattern;
Available signal-to-noise as a function of receiver gain and low-noise amplifier (LNA) noise figure;
Bandwidth, measured as: radiated power gain bandwidth; impedance bandwidth; or axial ratio bandwidth.
Gain and Radiation Pattern. Patch antennas are specified and usually used with an external ground plane, typically 70 or 100 millimeters (mm) square. Without an external ground plane a reasonable approximation of the radiation pattern is a circle tangential to the patch ground plane with a peak gain of about 3 dBic (dBic includes all power in a circular wave). The addition of an external ground plane increases the peak gain at zenith by up to 2 dB.
The pattern shown in Figure 4 is typical for a 25 mm patch on a 100 mm ground plane. The gain peaks just under 5 dBic, dropping to about 0 dB at an elevation angle of ±60 degrees (the horizon is 90 degrees).
FIGURE 4. Radiation pattern for 25 mm patch on 100 mm ground plane.
Table 1 tabulates approximate gain values at zenith for a range of GPS L1 patch sizes, mounted on a 100-mm ground plane, at resonance, radiated with a RHCP signals (that is, dBic).
TABLE 1. Patch size versus gain at zenith.
Clearly, gain is significantly lower for patches smaller than 25 mm square. Not illustrated here is that the bandwidths of antennas smaller than 25 mm also become too narrow for consideration for anything other than single-frequency signals such as GPS L1.
Achievable C/N0. The carrier signal-to-noise density ratio (C/N0) is a fundamental measure of signal quality and hence antenna performance. For a given receiver, if the C/N0 is degraded due to any cause, be it a poorly tuned patch or bad LNA noise figure or other, the shortfall in performance is non-recoverable.
The effective isotropic radiated power (EIRP) of the transmitted GPS L1 signal from the space vehicles is approximately 27 dBW. If D is the range to the satellite, and λ is the carrier wavelength, the free space path loss, PL, is given by
PL = [ λ / (4 × π × D)]2
The signal power received at the antenna terminals, Pr, is given by:
Pr = EIRP × Gr × PL
where Gr is the receive antenna gain.
The noise power in a 1 Hz bandwidth, N0, referred back to the antenna terminals is given by:
N0 = 10log(Te × k),
where Te is the overall system noise temperature, and k is the Boltzmann constant.
Thus C/N0, the ratio of received carrier power to noise in a 1 Hz bandwidth, referred to the antenna is
C/N0 = Pr / N0
Quantifying this calculation: For λ = 0.19 meters (corresponding to the L1 frequency), and an orbit height of 21,000 kilometers, the path loss,
PL = –182.8 dBW.
The received signal power,
Pr = EIRP(dBW) + Gr(dB)+ PL(dB)
(in dBW)
Assuming the mid-elevation antenna gain, Gr, is 3 dBic,
Pr = –152.8 dBW.
For a cascaded system such as a GPS receiver, the overall noise temperature is given by:
Te = Ts + Tlna + Tgps/Glna
where Te is the overall receiver system noise temperature, Tsis an estimate of sky-noise temperature at 1575.42 MHz, assumed to be 80 K, Tlna is the LNA noise temperature (76 K for an LNA noise figure of 1 dB), Glna is the LNA gain (631 for 28 dB gain), and Tgps is the noise temperature of the GPS receiver (636 K for 5 dB receiver noise figure).
Thus, Te = 157.1 K and N0 = –206.6 dBW.
The available ratio of received carrier power to 1 Hz noise, C/N0, referenced to the antenna is:
C/N0 = Pr/(Te × k) –
(implementation loss)
where implementation loss is an estimate of the decode implementation loss in the GPS receiver, assumed to be 2 dB (something of a fiddle factor, but reasonable!)
Thus, C/N0 = –152.8 – (–206.6) – 2 dB = 51.8 dB.
For satellites that subtend a high elevation angle, the reported C/N0 could be 2 dB higher or 53.8 dB best case.
A good circular antenna should provide C/N0 values in the range 51 dB–53 dB. This can be checked using the (NMEA) $GPGSV message output from most GNSS receivers. Comparative measurement of C/N0 provides the basis for comparative antenna evaluation as described later.
Single-Feed Bandwidth. Bandwidth of single-feed patches can be defined in several quite different ways.
Radiated power gain bandwidth: the bandwidth over which the amplitude at the terminals of the receiving antenna is not more than X dB below the peak amplitude, with an incident CP field.
Axial ratio bandwidth: the bandwidth over which the ratio of the maximum to minimum output signal powers for any two orthogonal axes is less than Y dB. This is an indicator of how well the antenna will reject cross-polarized signals.
Return loss (RL) or impedance bandwidth: that over which the feed input return loss is less than Z dB. This is very easy to measure, and gives the most optimistic bandwidth value.
The input impedance of a single-feed patch is shown in Figure 5. The rotated W-shape of the single-feed patch impedance is a result of the coupling between the two axes of the patch. The 10 dB return loss, called S11, is shown as a circle, outside of which |S11| > –10 dB.
These measures of bandwidth are shown for 25 × 25 × 4 mm and two thicknesses of 36 mm2 antennas in Table 2.
FIGURE 5. S11 for a 25 mm single-feed patch.TABLE 2. The various measures of patch bandwidth.
These different measures yield large differences in bandwidth. The merits of each depends on what is important to the user.
From a purist viewpoint, the most intuitively useful measure of bandwidth is the 0.5 dB radiated gain value. Even then, at the band edges so defined, the axial ratio for a 25 mm2 × 4 mm patch is degraded to about 5 dB, just on the negative side of ok.
As shown in Table 2, the 10 dB return loss bandwidth is comparatively wide. Figure 6 shows the EФ and Eϴ fields for a 36-mm patch a) at resonance and, b) and c), at the upper and lower –10 dB RL frequencies. At resonance the fields are equal, and the radiation is circular (add 3 dB for the CP gain). At the two 10 dB RL offset frequencies, the axial ratio is about 9 dB, with the dominant axis swapped at the band edges.
(a)
(b)
(c) FIGURE 6. (a) Realized gain patterns EФ and Eθ, single-feed at resonance, Fc. (b) realized gain patterns EФ and Eθ , single-feed, Fc+F–10 dB.
(c) realized gain patterns EФ and Eθ, single-feed, Fc-F+10dB.
As a transmitter, a 10 dB return loss would correspond to 90 percent of the energy transmitted, in this case, mostly on a single axis. By reciprocity, as a receiver, the single axis gain of the patch at the 10 dB RL frequency is higher (by about 2 dB ) than at resonance. So, if a linear response can be tolerated, the 10 dB bandwidth is a useful measure, albeit for a very non-ideal response.
Because the two axes are only balanced at resonance, single-feed patches are only truly circular at resonance. An ideal CP antenna has an equal response to a linearly polarized signal, for any rotational angle of incidence. Figure 7 shows the response of a CP antenna to a LP signal for any rotation, which is 3 dB down relative to the response to a co-polarized CP wave.
Figure 7. Perfect CP response to linearly polarized waveform.
In contrast, Figure 8 shows the responses of a single-feed patch (25 mm2 × 4 mm) as a function of field rotation with a linearlarly polarized wave. Note that, at resonance, all of the responses have the same amplitude because the patch is circular at that frequency.
Figure 8. 25-millimeter single-feed patch response to linear polarization rotation.
The responses shown above are for the following conditions:
A) single axis excitation (axis A)
B) single axis excitation (axis B)
C) equal axis excitation, antipodal
D) equal axis excitation, in-phase.
The relevance of this is that a circular polarized wave can become elliptical as a result of multipath interference. Figure 8 shows that the antenna response can be highly variable as a function of the angle of the ellipse principal axis. This is another way of looking at impaired cross-polarization rejection.
In addition, poor axial ratio results in non-equal contributions from each of EФ and Eϴ as the E vector of a linearly polarized wave is rotated. Thus an antenna with a poor axial ratio has a non-linear phase response, unlike a truly CP antenna which has an output phase that rotates proportionally with the E vector rotation.
25 mm2 patches for GPS/GLONASS applications are tuned to the mid frequency of 1590 MHz. Because the RHCP response is narrow, so is the cross polarization rejection, which is also centered at 1590 MHz, Figure 9 shows the simulated response of a single-feed 25 mm patch to co-polarized and cross polarized fields.
Figure 9. Co-polarized and cross polarized response, single-feed patch.
The cross-polarization rejection is degraded at both GPS and GLONASS frequencies, so that much of the ability of the antenna to reject reflected signals is lost.
Against these criteria, a 25 × 25 × 4 mm single-feed patch element can provide good CP performance over about 16 MHz. Of course, initial tuning tolerance must be subtracted from this. However, even within the 0.5 dB radiated gain bandwidth the axial ratio rapidly becomes degraded to about 5 dB, and at larger offsets, the patch response becomes virtually linearly polarized, with poor cross-polarization rejection and phase response. However, as a redeeming feature, the single-feed patch has a wideband frequency response albeit linearly polarized at the GPS and GLONASS frequencies (the band edges).
Dual-Feed Patches
By comparison, dual-feed patches can provide almost ideal characteristics over the bandwidth of the patch element. Figure 3 shows a typical physical configuration and a schematic representation for the feed combining network. This ensures that the two axis feeds are fully isolated from each other over all frequencies of interest. The well known 90-degree hybrid coupler provides exactly the required transfer function.
The Smith chart in Figure 10 shows the impedance of one of the two feeds (that is, one axis) and the combiner output impedance, this being just a small locus close to 50 ohms.
Figure 10. Dual-feed patch, single axis and combiner S11.
Contributions from each axis at all frequencies are theoretically identical for a perfect specimen, so that the configuration naturally has an almost ideal axial ratio (0 dB).
Gain and Radiation Pattern. At resonance, the mode of operation of the single and dual-feed patches is identical so, unsurprisingly, the gain and radiation pattern are also the same; see Figure 4.
Dual-Feed Bandwidth. The 1 dB radiation bandwidth of a dual-feed patch is just less than 1 MHz narrower than if configured as a single feed. Otherwise, the bandwidth of a dual-feed patch is simply the resonant characteristic of the cavities comprised of each axis. The allowable in-band roll-off defines the patch bandwidth, which in any event should not be worse than 1.0 dB, including initial tuning errors. The response for a 36 × 36 × 6 mm patch is shown in Figure 11.
Figure 11. Co-polarization and cross-polarization response, dual-feed patch.
Axial Ratio. Because the axial ratio of dual-feed patches is inherently good, the cross-polarization rejection is also good. The simulated cross-polarization response for the dual-feed patch is also shown in Figure 11.
In reality, small gain and phase imbalances in the printed circuit board, hybrid coupler, and patch itself will prevent the axial ratio from being perfect and cross-polarization response not quite so ideal. With good manufacturing controls, axial ratio can be held to typically better than 2 dB.
The obvious question is, since dual-feed devices have nearly ideal characteristics, why not just make a low cost small dual-feed antenna? There are three issues: The first is that the feed offsets required for a 25 mm2 patch are physically too close for two feed pins. Secondly, a dual-feed structure requires an additional relatively expensive combiner component; thirdly, sometimes, the only way to achieve the necessary bandwidth is through the considerably extended, but linearly polarized bandwidth of the single-feed patch.
That said, were it possible, it would be the ideal solution.
Comparative Performance
The C/N0 value reported in the NMEA $GPGSV message provides a simple method for comparative evaluation of GNSS antennas. The idea is to compare reported C/N0 values for a number of competing antenna types.
This requires a reference GPS receiver, a logging computer and the antennas to be evaluated, and these should be arranged so that:
The computer is set up to log the NMEA $GPGSV messages output from the receiver ($GLGSV for GLONASS).
Each antenna is placed and centered on identical ground planes (100 mm),
The antennas-under-test are not closer to each other than 0.5 meters (to ensure no coupling), and
Each antenna-under-test has a clear sight of the whole sky, and
It is possible to quickly switch the antenna connectors at the receiver.
The method is to connect each antenna in sequence for 15 seconds or so, and to log NMEA data during that time. The antenna connector substitution should be slick, so that the receiver quickly re-acquires, and to validate the assumption of a quasi-stationary constellation.
Each NMEA $GPGSV message reports C/N0, at the antenna, for up to 4 satellites in view. The best reported average C/N0 value for specific satellites 49 dB and above are the values of interest. The winner is the highest reported C/N0 value for each constellation.
This sequence should be repeated a few times to get the best estimate. The important parameter is the difference between the reported C/N0 and the receiver acquisition C/N0 threshold. If the acquisition C/N0 threshold is –30 dB, an antenna that yields –49 dB C/N0 has a 19 dB margin, while an antenna that yields 52 dB has a 22 dB margin — a big difference.
Immunity to LightSquared
Much has been written regarding the threat of the prospective terrestrial segment that the LightSquared L-band communication system poses for GPS (and GNSS in general), which mostly is true. On the other hand, front-end protection for GNSS antennas is a relatively simple, inexpensive addition. The performance cost (in addition to a very small dollar cost increment) is an unavoidable but relatively small sensitivity hit. Note that L-band augmentation systems, other than WAAS and compatible systems, face a more difficult problem.
This is not just a LightSquared issue. In several corners of the world, transmission of high-level signals are permitted that have the potential to interfere with GPS either by source distortion or inter-modulation within the GPS antenna front end itself.
The primary hazard is saturation of the first stage of what is usually a two stage LNA. So, the only way to protect against this is a pre-filter, as shown in Figure 12.
FIGURE 12. Pre-filtered antenna architecture.
There is a trade-off between the slope and corner frequency of the pre-filter out-of-band rejection and its associated insertion loss. The table below shows the response with a wider filter with an insertion loss of 1 dB, the second a more aggressive filter with a 2.5 dB insertion loss (IL).
Table 3 shows overall noise figure including and excluding sky noise. Sky-noise temperature is used here as a catchall that includes true sky-noise, thermal noise (the antenna can partially see the local environment), plus similar factors. The value used is arguable, but experience indicates this is a reasonable number.
The existence of sky noise limits the lowest available noise figure and sets the effect of a pre-filter in the correct context. In any event addition of a quite adequate pre-filter against a 1536 MHz signal can be achieved with less than 1 dB impact on received C/N0.
TABLE 3. Rejection and noise figure for pre-filtered antenna.
Putting It All Together
Small (25 mm2 × 4 mm) single-feed patches are only truly circularly polarized at resonance but do have good CP characteristics over a bandwidth of about 16 MHz, and almost perfect for GPS L1. The pre-dominance of this format for GPS L1 is fully justified.
However, when used to receive wider bandwidth signals such as GPS/GLONASS, single-feed patch antennas suffer from a litany of minor flaws, most particularly poor axial ratio and poor cross-polarization rejection.
On the other hand, the coupling that happens in single-feed antennas results in a very wide 10 dB return loss bandwidth but at the band edges (where the GNSS signals are) they are virtually linearly polarized.
There is no doubt that the performance of small single-feed patches for bandwidths such as those required for GPS/GLONASS coverage is marginal. However, to no small extent, the sensitivity of modern receiver chips is so good that marginal antenna performance can often be accommodated, at least from a basic operational viewpoint. The receiver bails out the antenna.
However, the end result must be degraded GNSS reception. If the application cannot tolerate reduced GNSS availability or accuracy because of marginal antenna performance the choice should be a dual-feed patch type. This will present the GNSS receiver with more consistent signals levels and phase responses and less interference. The end result should be faster acquisition, and realization of the improvement in horizontal dilution of precision (HDOP) that GPS/GLONASS offers.
The reported values of C/N0 in the $GPGCV NMEA message provides a simple and sensitive means to comparatively evaluate antenna performance.
A not insignificant consideration is that the antenna is usually a very visible part of a bigger system, and unavoidably represents the quality of the user equipment. In that case, the antenna housing robustness and appearance may also be a criterion to maintain the image of the end product.
The final point is that introduction of pre-filters into active GNSS is a good idea, whose time has come. This provides protection against the well known bug-a-boo, but also protects against known interference in other parts of the world.
Acknowledgments
I would like to acknowledge the assistance of Inpaq Technologies (Suzhou) Ltd., for provision of patch samples and technical support; Rony Amaya, adjunct research professor, Carleton University, Ottawa, for discussions and assistance in preparing this article; and STMicroeletronics for permission to cite the GPS+GLONASS demonstration video.
Gyles Panther is president and CTO of Tallysman Wireless (www.tallysman.com) and has an honors degree in applied physics from City University, London. He has worked in the fields of RF and satellite communications for more than 20 years. As CTO of a precursor company he was the principal engineer for the development of a wide-area Canadian differential GPS corrections system (CDGPS) receiver. Tallysman is a new start-up specializing in high-performance GNSS antennas and systems.
Shadow matching. The two GNSS mobile phones beside the middle one show additional possible user positions referenced by the along-street component of the standard point positioning (SPP) solution.
By Paul D. Groves, Lei Wang, and Marek K. Ziebart
GNSS positioning in dense urban areas is unreliable, with accuracy particularly poor in the cross-street direction. One solution is shadow matching, a new positioning technique that uses 3D building models to predict which satellites are visible from different locations and compares this with the measured satellite visibility to determine position. This article presents test results of a preliminary shadow-matching algorithm in a London urban canyon and discusses the practical implementation of the technique
Poor GNSS positioning accuracy is common in urban canyons where tall buildings block the direct line-of-sight (LOS) signals from many, sometimes most, of the satellites, effectively casting GNSS shadows over the adjacent terrain. Without direct signals from four or more satellites, an accurate position solution cannot be determined. Sometimes, a degraded position solution can be obtained by using signals that can only be received by reflection off a building, known as non-line-of-sight (NLOS) signals.
Using GLONASS in addition to GPS considerably enhances direct signal availability, and the ongoing deployment of Galileo and Compass will enhance it further. However, an urban canyon affects the geometry of the available GNSS signals as well as their number. Signals with lines of sight going across the street are much more likely to be blocked by buildings than signals with lines of sight going along the street (see Figure 1). As a result, the signal geometry, and hence the positioning accuracy, will be much better along the direction of the street than across the street. For example, for a building-height-to-street-width ratio of three and direct signals from four GNSS constellations, the cross-street position uncertainty can exceed 20 meters, while the along-street uncertainty is within 5 meters.
Figure 1. Signal geometry of GNSS satellites in an urban canyon (aerial perspective).
This level of accuracy is good enough for some applications but not others. Knowing which side of the street a pedestrian on is useful for visitor guidance and location-based advertising, while it is critical for guiding the blind and visually impaired and for augmented-reality applications. Similarly, lane-level positioning is important for advanced intelligent transportation systems that can direct individual vehicles in order to maximize traffic flow and prioritize emergency vehicles.
Improving GNSS positioning in urban canyons requires lateral thinking. If it’s not possible to calculate a sufficiently accurate position solution using the visible satellites, why not use the nonvisible satellites as well? This is exactly what shadow matching does. If you know where the buildings are and how big they are, you can deduce positional information from the knowledge that certain signals are blocked.
This requires a 3D model of a city’s buildings. These are becoming more accurate and widely available and have already been used to predict GNSS signal availability and multipath interference.
The principle of shadow matching is simple. Due to obstruction by buildings in urban canyons, signals from many GNSS satellites will be receivable in some parts of a street, but not others. Where each direct signal is receivable can be predicted using a 3D city model. Consequently, by determining whether a direct signal is being received from a given satellite, the user can localize their position to within one of two areas of the street. Figure 2 illustrates this. By considering other satellites, the position solution may be refined further, producing a much more accurate cross-street position solution than available from conventional GNSS positioning in this environment. Thus the observed signal shadowing is matched with the predicted shadowing to determine position.
Figure 2. The shadow-matching concept: using direct signal reception to localize position.
This concept of shadow matching, has been proven by mathematical modeling. Satellite visibility predictions using a 3D city model of London have been validated with real-world observation, demonstrating the practical potential of shadow matching. Here, shadow matching is brought from proof of concept one step further to practical demonstration. A preliminary but complete implementation of shadow matching has been developed and tested in London using real-world GPS and GLONASS measurements. The algorithm is described first, followed by the test results. We then discuss dealing with different types of signal propagation that occur in urban areas. and how to implement shadow matching in real time on a platform such as a smartphone.
Shadow-Matching Algorithm
A basic shadow matching algorithm may be broken down into four steps:
Perform standard point positioning (SPP) using GNSS pseudo-ranges to obtain an approximate user position.
Define the search area for the shadow-matching position solution, generating a set of possible user positions close to the approximate position solution.
Predict satellite visibility at each candidate position using the 3D city model.
Evaluate the similarity between predicted and observed satellite visibility at each position. The candidate position with the best match is deemed to be the shadow-matching solution. This process can be conducted epoch by epoch, so the GNSS user can be either static or dynamic.
Conventional Positioning. In the first step, SPP using GNSS pseudo-ranges is conducted to acquire an initial user position. In an urban environment, the accuracy will often be poor, partly due to contamination by NLOS signals. Consistency checking may be used to identify the NLOS signals and, where possible, remove them from the position solution.
Candidate Position Determination. As discussed earlier, signal geometry and hence positioning accuracy will be much better along the direction of the street than across the street. Therefore, in this preliminary shadow-matching algorithm, the along-street component of SPP solution is used as a reference to generate a set of possible user positions that vary in across-street direction only (shown by the two mobile phones beside the SPP solution on the opening page of this article).
A more advanced shadow-matching algorithm would also consider candidate positions in the along-street direction and would vary the size of its search area based on an assessment of the quality of the SPP solution. The smaller the search area, the more efficient the shadow-matching algorithm will be. However, the search area must be large enough to contain the true position. Further research is needed to determine the optimum search area.
Satellite Visibility Prediction. At each candidate position, the two-step building boundary method predicts satellite visibility from the 3D city model. First, a building boundary from a GNSS user’s perspective is determined for each azimuth (from 0 to 360°) as a series of elevation angles. The results from this step show where the building boundaries are located within an azimuth-elevation sky plot. Figure 3 shows an example of a building boundary computed from a possible user location. Once the building boundary has been computed, it may be stored and reused.
Figure 3. Example of a building boundary as azimuth-elevation pairs in a sky plot. (The centre of the plot correspond to a 90º elevation or normal incidence).
Next, each satellite elevation is compared with the building boundary elevation at the same azimuth. The satellite is predicted to be visible if it is above the building boundary. If the satellite is just within the building boundary, a potentially diffracted signal can be predicted. However, this feature was not included in the preliminary shadow-matching algorithm described here. A software toolkit for determining satellite visibility was developed in C++.
Figure 4 shows the relationships between its processes.
Figure 4. The process of satellite visibility prediction. (Click to enlarge.)
The building boundary approach is efficient where a great number of satellite visibility tests are performed at the same location. For real-time visibility determination, building boundaries may be pre-computed over a grid of possible user locations and stored. However, there is an alternative. Instead of computing building boundaries, each satellite LOS can be directly compared with the city model to determine if it is blocked by buildings. This single LOS method is more efficient overall where only a few satellite visibility tests are performed at a given location. However, for real-time, it imposes a much higher processing load than using pre-computed building boundaries.
In practice, either method may be employed, depending on the situation. For real-time shadow matching, the trade-off is between a higher processing load for the single LOS method and greater data storage for the building boundary method. For non-real-time visibility determination, the trade-off depends on the number of tests required at each location.
Matching Prediction and Observation. The final step, evaluating the similarity between predicted and observed satellite visibility at each position and identifying the best match as the shadow matching solution, comprises three functions: satellite matching, position scoring, and position comparison.
Satellite matching determines for each satellite the degree of similarity between the predicted satellite visibility and the real observation. Figure 5 shows the simple satellite matching function deployed for this study. For each satellite above a pre-set elevation mask angle, if the prediction agrees with the observation, the score is one; otherwise, the score is zero.
Figure 5. Scoring matrix giving the score for each satellite in shadow matching.
Future research will be conducted to extend the matching function so that different scores are produced for signals predicted to be in the diffraction region and signals observed with low and medium signal-to-noise levels.
Position scoring evaluates the overall degree of match between predicted and observed satellite visibility for each possible user position, summing up the satellite matching scores for each candidate position to give a position score.
Finally, position comparison selects the candidate position with the highest overall score and outputs this as the position solution. However, sometimes there is more than one candidate. Further research is needed to find the optimum way of determining a positioning solution with associated error bounds from a grid of shadow-matching scores.
Figure 6 summarizes the shadow-matching process.
Figure 6. The shadow matching process. Blue denotes input data, red denotes main process steps, and white denotes intermediate or final results. (Click to enlarge.)
Experimental Verification
We used a 3D city model of the Aldgate area of central London to test shadow matching. The model has a high level of detail and decimeter-level accuracy.
The software toolkit developed for this study stores and processes 3D city model data using Virtual Reality Modeling Language (VRML), an international standard format. Model data in other formats can be transformed to VRML. Buildings in VRML format are represented by structures, which in turn comprise polygons (normally triangle meshes).
Methodology. Experimental data were collected in a highly built-up area in central London, using two multi-constellation survey-grade GNSS receivers, logging 1 Hz data simultaneously (note that shadow matching does not require two receivers). As shown in Figure 7, they were set up on the north and south sidewalks of Fenchurch Street.
Figure 7. True position of receivers against conventional standard point positioning (SPP) solution. Pins show the true positions and bubbles the SPP solutions; the numbers and colors indicate the receiver.
For the first step of shadow matching, software was used to conduct SPP processing using GPS and GLONASS signals. Only L1 pseudo-ranges were used to acquire an initial user position. It can be seen in Figure 7 that the conventional SPP solutions have significant offsets from the true positions (16–31meters for receiver A and 18–24 meters for receiver B). As receiver B suffers more signal blockage from buildings, it has fewer epochs with four or more satellites in view, so fewer successful SPP GNSS solutions were obtained. Although they have significant offsets in the across-street direction, they are consistent and agree much more with the receivers’ true positions in the along-street direction. This result verifies the assumption made in the shadow-matching algorithm that the accuracy of the along-street SPP positioning solution is much better than in the across-street direction.
Four candidate user positions were selected using the common along-street position of the conventional SPP solution. They are distributed across the sidewalks and vehicle lanes, on both sides of the street. Figure 8 illustrates this. Note that candidate 3 is the true position of receiver A and candidate 0 is the true position of receiver B.
Figure 8. Candidate user positions (in yellow) and true receiver positions (in red and green) in the shadow matching experimental verification.
Satellite visibility was predicted individually for each of the four positions. Then each was compared with the real data observed from the two GNSS receivers. Figure 8 shows part of the architectural city model of London used to predict the satellite visibility.
Results. The experimental results are shown in three stages: the satellite visibility comparison between prediction and observation, the candidate position scoring function, and the success rate for each candidate location. The primary success criterion is whether the algorithm is able to determine the correct side of the street. A secondary aim is to test whether the algorithm can distinguish between the sidewalk and vehicle lane on the same side of the street.
To show the degree of agreement or disagreement between the predicted and observed satellite visibilities, time series for each of the four candidate positions are compared with each of the two receivers’ experimental data. The results are shown in eight graphs. Figure 9 compares data from receiver A for each of four possible user locations. Figure 10 shows the same comparison for receiver B. The time window was from 13:56:30 to 14:06:30 (UTC). G denotes GPS satellites and R refers to GLONASS satellites.
In Figure 9, the green and blue dots indicate an agreement between prediction and observation for the candidate user position, while the orange and red colors represent their disagreement. Thus, a larger number of cases of green and blue indicate a better match between the candidate user location and the observations. Therefore, such a candidate is more likely to be close to the receiver’s true position.
Figures 9 and 10 clearly show that the closer the candidate position is to the true position, the greater the agreement between predictions and observations.
Figure 9. Comparison of satellite visibility between receiver A and candidate user locations (candidate user point 3 is the true position). (Click to enlarge.)Figure 10. Comparison of satellite visibility between receiver B and candidate user locations (candidate user point 0 is the true position). (Click to enlarge.)
Even at the correct candidate location, there is not complete agreement between the observations and predictions. A number of signals were observed but not predicted. These had signal-to-noise levels 8 dB or more lower than the predicted signals and are most likely due to reflection and/or diffraction.
However, shadow matching does not require complete agreement in order to work. In this test, no signals were predicted but not observed at the correct location.
To complete shadow matching, we evaluate each candidate position by summing up the number of satellites common to both the predictions and real observations. Figure 11 and Figure 12 show the results of the summation for receivers A and B, respectively.
Figure 11. Evaluation of similarity — number of common satellites in view between each candidate user point and receiver A.Figure 12. Evaluation of similarity — number of common satellites in view between each candidate user point and receiver B.
It is clear from Figure 11 that among four possible user positions, point 3 is the one with the highest agreement score with the observations from receiver A. As shown in the right-bottom graph, for about half the epochs, visibility predictions for all 12 satellites above the masking angle match the real observations.
As shown in Figure 12, for receiver B, the time series of the agreement score is generally better at the true location (Point 0) than at other points. However, the level of agreement between predictions and observations is not as good as at receiver A‘s location.
To judge the performance of shadow matching, the selection rate of each of the four candidate user positions for each of the two receivers was computed by dividing the number of times that position was selected by shadow matching by the number of epochs. Where the same score was attained for two or more positions, each position was considered partially selected. For example, if two positions have the same score, then each of them is considered half-selected.
The selection rate results are shown in Figure 13. For receiver A, the shadow matching algorithm correctly indicated the true position among the four candidates 100 percent of the time. This means the algorithm successfully distinguished between the two sides of the street, and further distinguished between a user on the sidewalk and a user in the vehicle lane. For receiver B, the algorithm identified the correct side of the street (Points 0 and 1) 94.65 percent of the time, and the correct location among the four candidates in 81.29 percent of the epochs evaluated.
Taking the average of the two test sites, the correct side of the street was identified 97.3 percent of the time and the correct position from the four candidates 90.6 percent of the time.
Figure 13. Candidate position selection rate for receiver A (left) and receiver B (right).
Practical Implementation
The basic shadow-matching algorithm operates under the assumption that GNSS signals are either directly visible or blocked by a building. However, in reality, signals can also be received via indirect paths due to reflection or diffraction. This was observed at both locations during the tests. As shadow matching seeks the position with the best match, rather than looking for a perfect match, it can tolerate a certain number of these signals and still identify the correct position.
These tests were performed using survey-grade user equipment with a relatively high tracking threshold, so the weakest signals are not observed. Furthermore, the antenna has strong polarization discrimination so exhibits a low gain for reflected signals. However, for shadow matching to be practical, it should also work on a smartphone, which typically combines a high-sensitivity receiver with a linearly polarized antenna, which does not distinguish between direct and reflected signals. Consequently, a smartphone receiver is likely to observe more reflected and diffracted signals.
NLOS reflected and diffracted signals are weaker than directly-received signals, so the shadow-matching algorithm could be modified such that only signals received above a certain signal-to-noise threshold are classified as observed. However, this would introduce a new problem: signals received via a direct LOS path but attenuated by a person’s body would be classified as not observed, even though they would be predicted to be visible at the correct location. The same problem would occur where the LOS coincides with a direction in which the antenna is weak. Consequently, to get the best performance from shadow matching, several different categories of observed signal should be considered in the scoring matrix.
Diffraction occurs when the LOS is just inside the building boundary. Therefore, the 3D city model can be used to predict when a diffracted signal may be received. However, it cannot easily be used to predict the signal-to-noise level of that signal because diffraction patterns are complex. Therefore, shadow matching can potentially be improved by adding a third prediction category for diffraction. Figure 14 shows a posssible optimized scoring matrix with values between 0 and 1 for the new categories to be determined empirically, possibly as functions of the measured signal-to-noise. Different scoring matrices may be suited to pedestrian and vehicle applications and to different user equipment designs.
Directly-received signals are also affected by multipath interference. However, this will not normally impact shadow matching as it does not affect whether a signal is received or not.
Shadow matching has been demonstrated using both GPS and GLONASS measurements. The more signals available for shadow matching, the better the expected accuracy and reliability. Thus, the addition of Galileo, Compass, and regional systems, including SBAS, should improve performance. However, further research is needed to determine whether shadow matching using GPS alone is viable. Combining data from multiple epochs should also improve shadow matching performance, particularly where the user is moving.
A practical shadow-matching algorithm must be implementable in real time on a mobile device. Three models maybe considered.
A network-based solution, whereby GNSS measurements are transmitted to a server, which stores the building boundary data, computes a solution and then sends it to the user.
A handset-based solution, where the shadow-matching algorithm is run on the handset, which also stores the building boundary data.
A hybrid model, whereby the shadow-matching algorithm runs on the handset, but the building boundary data is streamed from a server as and when required.
Using stored or streamed building boundaries, fewer than fifty comparison and addition operations are required to calculate an overall shadow-matching score for one candidate position with two GNSS constellations. Therefore, shadow matching may be performed in real time on a mobile device with several hundred candidate positions, where necessary.
Without any data compression, about 300 bytes are required to store a building boundary with a 1° resolution. If a 2×2 meter grid spacing is used for the candidate positions, a 1-kilometer long, 20-meter wide street will contain 5000 grid points, requiring 1.5 MB of data storage. By exploiting the similarities both between neighboring azimuths in the same building boundary and between building boundaries at neighboring grid points, substantial data compression should be achievable; possibly up to a factor of ten.
Therefore, a standard 4 GB flash drive could store building boundary data for 2,500–25,000 kilometers of road network. For comparison, the Greater London metropolitan area contains about 15,000 km of road. However, as shadow matching is only useful in streets where conventional GNSS positioning is poor, the database need only contain building boundary data for these streets, maybe 10 percent of the total. Therefore, it should be practical to preload a mobile device with shadow-matching data for several cities, which could be kept up-to-date via the internet.
An alternative model is to download the building boundary data from a network server as required. A conventional GNSS position solution or Wi-Fi fix should be able to localize position to within 1,000 grid points, requiring 30–300 kB of building boundary data to be downloaded in order to perform shadow matching. This takes less than two seconds using a 3G mobile phone connection with an average data rate.
In practice, shadow matching, would be implemented as part of a wider intelligent urban positioning system. This shadow-matching algorithm assumes that the user is outdoors. Indoor operation, if viable, would require a different approach. It is necessary to determine the error bounds of the conventional GNSS position solution, not only to determine the search area for shadow matching, but decide whether shadow matching should be performed at all. For example, in a completely open-sky environment, shadow matching will fail, but a good position solution will be obtainable conventionally.
There are other ways in which 3D city models could be used to improve GNSS positioning. For example, they could aid identification of NLOS and multipath-contaminated signals and, in principle, even correction of NLOS ranging measurements. Intelligent urban positioning can also incorporate additional sensors, such as odometers on cars and cell phone signals, WiFi and inertial sensors for pedestrian users.
Next Steps
Three potential future lines of research stem from this work: improving the initialization from conventional GNSS positioning, improving of satellite visibility predictions with ambiguous observations, and optimizing the position determination from the shadow-matching scores. In addition, performance will be evaluated over a wider range of environments.
Acknowledgments
The authors gratefully acknowledge Kimon Voutsis for his support with the experiments. This work has been jointly funded by the University College London Engineering Faculty Scholarship Scheme and the Chinese Scholarship Council. This article is based on a paper presented at the 2011 European Navigation Conference in London, organized by the Royal Institute of Navigation.
Manufacturers
The tests used two Leica Viva GS15 GNSS receivers. Leica Geo-office software performed SPP processing of GPS and GLONASS signals. ZMapping Ltd. supplied the London 3D model.
Paul Groves is a member of the academic faculty at University College London (UCL), where he leads the Space Geodesy and Navigation Laboratory’s program of research on robust navigation and positioning. He is also author of the book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems.
Lei Wang is a Ph.D. student at UCL. He received a Bachelor’s degree in geodesy and geomatics from Wuhan University in 2010. His current research interests are GNSS-based positioning techniques for urban canyons.
Marek Ziebart is professor of space geodesy, director of the Space Geodesy and Navigation Laboratory, and vice dean for research of UCL Engineering.
In the next two to four years, mobile device location platforms will be able to provide positioning performance that enables emergency call (E911) and location-based services (LBS) with excellent accuracy (5–10 meters) in all locations. We call this accurate everywhere location, and it will be a significant enabler of indoor navigation applications and for even wider adoption of consumer LBS.
In fact, we may eventually forget how we ever lived without it. This technology can enhance our lives by enabling our mobile devices to know precisely where we are at all times. Armed with this information, our devices can behave in a way that suits our specific situation, and they can do this without us having to do anything other than keep the phone with us.
Text and images will get significantly bigger while driving or walking. Facebook notifications can be automatically disabled while at work. Shopping lists can be automatically displayed when approaching a store that has an item on the list. The potential benefits are endless — provided that the privacy issues associated with location are handled appropriately.
GNSS is the superior technology when a mostly unobstructed sky is available, but it can’t deliver accurate position fixes in all environments — at least not at a cost and in a form factor that works for consumer mobile devices. Accurate everywhere location requires some form of advanced hybrid location technology. Because its definition is constantly evolving, the term hybrid can mean different things to different people. This article aims to clear that up.
Here is an overview of the hybrid positioning technology currently used in mobile devices, as well as what is coming in the next two to four years that will enable accurate everywhere location:
GPS + GLONASS. Multiple GNSS technologies are starting to be more common in new chipsets aimed at mobile devices, and assisted-GPS (A-GPS) + A-GLONASS is right around the corner. The benefit from this hybrid GNSS approach is that with more satellites in the sky, devices are likely to receive more line-of-sight signals in challenging environments where a significant portion of the sky is obstructed (like urban canyons). While this might improve performance on a street in downtown Manhattan, it does not help when you are in the middle of a building or in the subway.
Cellular Multilateration + A-GNSS. Mobile devices with CDMA cellular radios have supported hybrid A-GPS + advanced forward-link trilateration (AFLT) for more than a decade. This concept is now being applied to long-term evolution (LTE) devices, with support for A-GNSS + observed time difference of arrival (OTDOA) being written into the 3GPP standards. Both AFLT and OTDOA are forms of cellular multilateration, which means that devices can make measurements of relative timing offsets between multiple downlink cellular signals, and those measurements can be used in a hyperbolic multilateration formula to compute a position (one signal acts as reference and hyperbolic intersection of 2+ signals are used for position).
Does this sound familiar? It happens to be very similar to GNSS location computation, so it is possible to combine measurements from cellular signals and measurements from GNSS satellites to compute a hybrid position. For example, 2 satellites + 2 cellular measurements can be combined to compute a position, which makes this technique very attractive. Although it is used for both E911 positioning in North America and LBS worldwide, this technology will become even more widespread as LTE adoption increases.
A-GNSS + Wi-Fi Positioning + eCID. Many popular smartphones today support Wi-Fi positioning and enhanced cell ID (eCID) in addition to A-GNSS. This hybrid solution allows coarse positioning in indoor environments where A-GNSS does not work. Solutions for Wi-Fi and eCID positioning are currently very fragmented and proprietary. However, this is the reason you are able to get a semi-accurate position fix on your Android or iOS mobile device when GNSS satellites are impossible to measure (many other devices support this as well). These technologies are going to provide more accurate information as time goes on, but we don’t believe they will achieve accurate everywhere location on their own.
A-GNSS + Wi-Fi Positioning + Cellular Positioning + Sensors. You might have guessed it, but we think accurate everywhere location will be enabled by a combination of all the above hybrid techniques plus one more important technology: sensors. Integrated sensors like accelerometers, magnetometers, and barometers enable devices to sense changes in direction, orientation, and elevation. Given an accurate starting location (for example, GNSS position fix), sensors can track location accurately for several minutes (and this will continue to get better). Location error will accumulate over time, but this can be minimized when Wi-Fi, cellular, and GNSS positioning are used in conjunction to constrain the error. Furthermore, barometers can be used to track elevation changes, thereby allowing devices to know exactly what floor of a building a user is on. Other technologies, or signals of opportunity, may be used in the future to further improve performance, but we think this mix of A-GNSS, Wi-Fi, cellular, and sensor positioning is the key to accurate everywhere location in mobile devices.
With substantial R&D dollars being spent now, and standardized testing for hybrid positioning emerging this year, our best estimate is that the accurate everywhere technology will become commercially widespread by 2015.
Brock Butler is director of Spirent’s Wireless Location Technologies, part of a team that has made major contributions to development of the LBS standards in the 3GPP: Spirent filled the editor and rapporteur roles for the TS 51.010 and TS 34.171 A-GPS Terminal Conformance Specifications, as well as the editor role for the Enabler Test Specification for SUPL in the OMA. Butler holds a BSc in electrical engineering from Villanova University.
The Czech government signed an agrement January 27 with the European GNSS Agency (GSA) for Prague to host the headquarters of the Galileo system. The signing took place during the Galileo Application Congress Prague 2012.
Paving the way for the Agency’s presence in the Czech Republic, the host agreement was jointly signed by Pavel Dobeš, minister of Transport, and Carlo des Dorides, executive director of the GSA, in the presence of Petr Nečas, prime minister of the Czech Republic and Antonio Tajani, vice president of the European Commission responsible for industry and entrepreneurship. The accord will see the GSA moved to Prague later this year.
The Galileo Applications Congress in Prague drew experts from around Europe and around the world to discuss Galileo and possible services. representatives of the European Union, the European Space Agency (ESA) and the GSA discussed their future roles in Europe's GNSS programmes, Galileo and EGNOS. The event also took place against a backdrop of key changes in how Europe's flagship GNSS programmes are governed.
"This is a good moment to take stock of where we are and where we are going with Galileo," said GSA Executive Director Carlo Des Dorides. "The focus is on the future, with an expanded mission for our Agency. What we can say now is that the future is bright; the market for new GNSS technologies and services, many of which you will hear about during this congress, will continue to grow, in spite of the current difficult economic conditions."
Under the current European Commission proposal for a new GNSS governance arrangement, the GSA would be charged with the commercialisation and exploitation of Galileo and EGNOS services, including the operations of the Galileo security monitoring centers to be deployed in the UK and France. The Commission itself would provide the policy framework and political support, while ESA would provide the engineering competence. And while some details still need to be clarified, including how the interfaces between these three bodies would operate, most opinions seem to be moving quickly into line with the proposal.
ESA Director General Jean-Jacques Dordain said the measure of Galileo's success will not be in the number of satellites placed in orbit but in the quality of its services. "The very existence of the GSA as the service provider is a key to this success," he said. "Working to support the GSA, therefore, will also be ESA's objective, and we are committed to seeing this happen."
NEWTON, Mass. — January 30, 2012 — Questex Media announced today that Alan Cameron, editor-in-chief of GPS World, has been appointed publisher of the magazine, its website, eight e-mail newsletters, a geographic information systems counterpart Geospatial Solutions, and other B-to-B communications vehicles supporting the international positioning, navigation, timing, and mapping industries.
GPS World, the flagship of the enterprise, covers the U.S. Global Positioning System, Russia’s GLONASS constellation, Europe’s Galileo satellite navigation system, China’s Beidou/Compass system, and Japan’s QZSS. GPS World was the first publication in the market, launched over 20 years ago, and continues to be the market’s leading publication. GPS World is also the only publication in the industry that offers an audited circulation.
“The world is entering the Golden Age of GNSS (global navigation satellite systems),” said Kevin Stoltman, Vice President of the Industrial Specialty Group at Questex Media. “The market for GPS- and GNSS-related technologies continues to expand dramatically, changing as it goes. Governments in North America, Europe, Russia, Asia, and the Pacific are all actively funding and growing their systems, and companies from large corporations to small enterprises are fielding new products and services daily.”
“Cameron will continue engaging actively with industry leaders, helping the community meet new challenges and capitalize on new opportunities,” Stoltman added. “He is spearheading significant initiatives, spanning both print and online, to more effectively serve the global audience.”
Cameron has played a key role at GPS World since joining the publication as senior editor in 2000. He speaks at many association- and government-sponsored meetings and forums, organizes an annual Leadership Dinner for the industry, has launched several electronic publications including a Digital Edition of the print magazine, and pioneered blogs and social media use in the community.
He became editor-in-chief of the magazine in 2006, and now adds new business development, advertising sales, print and digital circulation, marketing, and strategic planning to his direct and supervisory duties.
“We invite comment on LightSquared’s petition, and establish a pleading cycle.” Thus spake the Federal Communications Commission (FCC), groping for a way forward in the ongoing LightSquared/GPS conflict. The FCC has opened an Internet docket for public comment on the LightSquared position that GPS users and receivers “do not merit legal protection from interference” created by LightSquared. The FCC asks for comments by February 27.
LightSquared asked the FCC in December to rule that GPS receivers and users “do not merit legal protection from interference” caused by the proposed wireless broadband service. Such interference has been amply demonstrated by comprehensive testing from May to October of last year. Opening the docket for public comment is the FCC’s way of fielding the LightSquared petition.
LightSquared claimed in its December 20 petition that GPS makers sell “unlicensed and poorly designed” receivers that improperly listen to LightSquared’s airwaves.
Jim Kirkland, general counsel of Trimble Navigation Ltd. and head of the Save Our GPS Coalition, responded that Congressional directives bar the FCC from clearing LightSquared before questions of GPS interference are settled. The company’s December requests consists of “gross mischaracterization of prior FCC decisions,” Kirkland stated. “LightSquared and its predecessors have never been allowed to interfere with GPS.”
Parties are invited to file comments in response to LightSquared’s petition for declaratory ruling in IB Docket No. 11-109 or ET Docket No. 10-142, no later than February 27. Parties may file replies in response to those comments in IB Docket No. 11-109 or ET Docket No. 10-142, as appropriate, no later than March 13.
Click here for the FCC Public Notice, “International Bureau Establishes Pleading Cycle for LightSquared Petition for Declaratory Ruling.”
