Washington, D.C. — The Federal Communications Commission’s Enforcement Bureau today launched a dedicated jammer tip line – 1-855-55-NOJAM (or 1-855-556-6526) – to make it easier for the public to report the use or sale of illegal GPS, cell phone or other signal jammers. It is against the law for consumers to use, import, advertise, sell or ship a GPS or cell jammer or any other type of device that blocks, jams or interferes with authorized communications, whether on private or public property.
The FCC asks people to call the toll-free Jammer Tip Line immediately if:
you are aware of the ongoing use of a cell, GPS, or other signal jammer;
your employer operates a jammer in your workplace;
you observe a jammer in operation at your school or college;
you observe an advertisement for a jammer at a local store; or
you observe a jammer being operated on your local bus, train or other mass transit system.
“We need consumers to be our eyes and ears. Jammers do not just weed out noisy or annoying conversations and disable unwanted GPS tracking, they can prevent 9-1-1 and other emergency phone calls from getting through in a time of need,” Michele Ellison, chief of the Enforcement Bureau, said.
Calls to the Jammer Tip Line will be handled by experienced Enforcement Bureau staff. Callers are encouraged to provide as much detail as possible, including the time and location of the incident, a description of the jamming device (if available), and the name and contact information of the individual or business using or selling the device.
While callers may remain anonymous, the bureau urges callers to provide a contact phone number in case additional information is needed. “Every tip can make a difference,” Ellison said. “While our agents are actively pursuing these violations online and on the street, you can help. We encourage concerned parents, commuters, employees, and anyone else with credible information to tip us off. Working together, we can stop the spread of illegal jammers.
For more information, Frequently Asked Questions about cell, GPS, and Wi-Fi jammers are available at www.fcc.gov/jammers, or email [email protected].
Figure 1. Distribution of the GPS+COMPASS tracking network established by the GNSS Research Center at Wuhan University and used as test network in this study.
Data from a tracking network with 12 stations in China, the Pacific region, Europe, and Africa demonstrates the capacity of Compass with a constellation comprising four geostationary Earth-orbit (GEO) satellites and five inclined geosynchronous orbit (IGSO) satellites in operation. The regional system will be completed around the end of 2012 with a constellation of five GEOs, five IGSOs, and four medium-Earth orbit (MEO) satellites. By 2020 it will be extended into a global system.
By Maorong Ge, Hongping Zhang, Xiaolin Jia, Shuli Song, and Jens Wickert
China’s satellite navigation system Compass, also known as BeiDou, has been in deveopment for more than a decade. According to the China National Space Administration, the development is scheduled in three steps: experimental system, regional system, and global system.
The experimental system was established as the BeiDou-1 system, with a constellation comprising three satellites in geostationary orbit (GEO), providing operational positioning and short-message communication. The follow-up BeiDou-2 system is planned to be built first as a regional system with a constellation of five GEO satellites, five in inclined geosynchronous orbit (IGSO), and four in medium-Earth orbit (MEO), and then to be extended to a global system consisting of five GEO, three IGSO, and 27 MEO satellites. The regional system is expected to provide operational service for China and its surroundings by the end of 2012, and the global system to be completed by the end of 2020.
The Compass system will provide two levels of services. The open service is free to civilian users with positioning accuracy of 10 meters, timing accuracy of 20 nanoseconds (ns) and velocity accuracy of 0.2 meters/second (m/s). The authorized service ensures more precise and reliable uses even in complex situations and probably includes short-message communications.
The fulfillment of the regional-system phase is approaching, and the scheduled constellation is nearly completed. Besides the standard services and the precise relative positioning, a detailed investigation on the real-time precise positioning service of the Compass regional system is certainly of great interest.
With data collected in May 2012 at a regional tracking network deployed by Wuhan University, we investigate the performance of precise orbit and clock determination, which is the base of all the precise positioning service, using Compass data only. We furthermore demonstrate the capability of Compass precise positioning service by means of precise point positioning (PPP) in post-processing and simulated real-time mode.
After a short description of the data set, we introduce the EPOS-RT software package, which is used for all the data processing. Then we explain the processing strategies for the various investigations, and finally present the results and discuss them in detail.
Tracking Data
The GNSS research center at Wuhan University is deploying its own global GNSS network for scientific purposes, focusing on the study of Compass, as there are already plenty of data on the GPS and GLONASS systems. At this point there are more than 15 stations in China and its neighboring regions.
Two weeks of tracking data from days 122 to 135 in 2012 is made available for the study by the GNSS Research Center at Wuhan University, with the permission of the Compass authorities. The tracking stations are equipped with UR240 dual-frequency receivers and UA240 antennas, which can receive both GPS and Compass signals, and are developed by the UNICORE company in China. For this study, 12 stations are employed. Among them are seven stations located in China: Chengdu (chdu), Harbin (hrbn), HongKong (hktu), Lhasa (lasa), Shanghai (sha1), Wuhan (cent) and Xi’an (xian); and five more in Singapore (sigp), Australia (peth), the United Arab Emirates (dhab), Europa (leid) and Africa (joha). Figure 1 shows the distribution of the stations, while Table 1 shows the data availability of each station during the selected test period.
Table 1. Data availability of the stations in the test network.
There were 11 satellites in operation: four GEOs (C01, C03, C04, C05), five IGSOs (C06, C07, C08, C09, C10), and two MEOs (C11, C12). During the test time, two maneuvers were detected, on satellite C01 on day 123 and on C06 on day 130. The two MEOs are not included in the processing because they were still in their test phase.
Software Packages
The EPOS-RT software was designed for both post-mission and real-time processing of observations from multi-techniques, such as GNSS and satellite laser ranging (SLR) and possibly very-long-baseline interferometry (VLBI), for various applications in Earth and space sciences. It has been developed at the German Research Centre for Geosciences (GFZ), primarily for real-time applications, and has been running operationally for several years for global PPP service and its augmentation. Recently the post-processing functions have been developed to support precise orbit determinations of GNSS and LEOs for several ongoing projects.
We have adapted the software package for Compass data for this study. As the Compass signal is very similar to those of GPS and Galileo, the adaption is straight-forward thanks to the new structure of the software package. The only difference to GPS and Galileo is that recently there are mainly GEOs and IGSOs in the Compass system, instead of only MEOs. Therefore, most of the satellites can only be tracked by a regional network; thus, the observation geometry for precise orbit determination and for positioning are rather different from current GPS and GLONASS.
Figure 2 shows the structure of the software package. It includes the following basic modules: preprocessing, orbit integration, parameter estimation and data editing, and ambiguity-fixing. We have developed a least-square estimator for post-mission data processing and a square-root information filter estimator for real-time processing.
Figure 2. Structure of the EPOS-RT software.
GPS Data Processing
To assess Compass-derived products, we need their so-called true values. The simplest way is to estimate the values using the GPS data provided by the same receivers.
First of all, PPP is employed to process GPS data using International GNSS Service (IGS) final products. PPP is carried out for the stations over the test period on a daily basis, with receiver clocks, station coordinates, and zenith tropospheric delays (ZTD) as parameters. The repeatability of the daily solutions confirms a position accuracy of better than 1 centimeter (cm), which is good enough for Compass data processing. The station clock corrections and the ZTD are also obtained as by-products.
The daily solutions are combined to get the final station coordinates. These coordinates will be fixed as ground truth in Compass precise orbit and clock determination. Compass and GPS do not usually have the same antenna phase centers, and the antenna is not yet calibrated, thus the corresponding corrections are not yet available. However, this difference could be ignored in this study, as antennas of the same type are used for all the stations.
Orbit and Clock Determination
For Compass, a three-day solution is employed for precise orbit and clock estimation, to improve the solution strength because of the weak geometry of a regional tracking network. The orbits and clocks are estimated fully independent from the GPS observations and their derived results, except the station coordinates, which are used as known values.
The estimated products are validated by checking the orbit differences of the overlapped time span between two adjacent three-day solutions. As shown in Figure 3, orbit of the last day in a three-day solution is compared with that over the middle day of the next three-day solution. The root-mean-square (RMS) deviation of the orbit difference is used as index to qualify the estimated orbit.
Figure 3. Three-day solution and orbit overlap. The last day of a three-day solution is compared with the middle day of the next three-day solution.
In each three-day solution, the observation models and parameters used in the processing are listed in Table 2, which are similar to the operational IGS data processing at GFZ except that the antenna phase center offset (PCO) and phase center variation (PCV) are set to zero for both receivers and satellites because they are not yet available.
Satellite force models are also similar to those we use for GPS and GLONASS in our routine IGS data processing and are listed in Table 2. There is also no information about the attitude control of the Compass satellites. We assume that the nominal attitude is defined the same as GPS satellite of Block IIR.
Table 2. Observation and force models and parameters used in the processing.
Satellite Orbits. Figure 4 shows the statistics of the overlapped orbit comparison for each individual satellite. The averaged RMS in along- and cross-track and radial directions and 3D-RMS as well are plotted. GEOs are on the left side, and IGSOs on the right side; the averaged RMS of the two groups are indicated as (GEO) and (IGSO) respectively. The RMS values are also listed in Table 3.
As expected, GEO satellites have much larger RMS than IGSOs. On average, GEOs have an accuracy measured by 3D-RMS of 288 cm, whereas that of IGSOs is about 21 cm.
As usual, the along-track component of the estimated orbit has poorer quality than the others in precise orbit determination; this is evident from Figure 4 and Table 3. However, the large 3D-RMS of GEOs is dominated by the along-track component, which is several tens of times larger than those of the others, whereas IGSO shows only a very slight degradation in along-track against the cross-track and radial. The major reason is that IGSO has much stronger geometry due to its significant movement with respect to the regional ground-tracking network than GEO.
Figure 4. Averaged daily RMS of all 12 three-day solutions. GEOs are on the left side and IGSOs on the right. Their averages are indicated with (GEO) and (IGSO), respectively.Table 3. RMS of overlapped orbits (unit, centimeters).
If we check the time series of the orbit differences, we notice that the large RMS in along-track direction is actually due to a constant disagreement of the two overlapped orbits. Figure 5 plots the time series of orbit differences for C05 and C06 as examples of GEO and IGSO satellites, respectively. For both satellites, the difference in along-track is almost a constant and it approaches –5 meters for C05.
Note that GEO shows a similar overlapping agreement in cross-track and radial directions as IGSO.
Figure 5. Time series of orbit differences of satellite C05 and C06 on the day 124 2012. A large constant bias is in along-track, especially for GEO C05.
Satellite Clocks. Figure 6 compares the satellite clocks derived from two adjacent three-day solutions, as was done for the satellite orbits. Satellite C10 is selected as reference for eliminating the epoch-wise systematic bias. The averaged RMS is about 0.56 ns (17 cm) and the averaged standard deviation (STD) is 0.23 ns (7 cm). Satellite C01 has a significant larger bias than any of the others, which might be correlated with its orbits.
From the orbit and clock comparison, both orbit and clock can hardly fulfill the requirement of PPP of cm-level accuracy. However, the biases in orbit and clock are usually compensatable to each other in observation modeling. Moreover, the constant along-track biases produce an almost constant bias in observation modeling because of the slightly changed geometry for GEOs. This constant bias will not affect the phase observations due to the estimation of ambiguity parameters. Its effect on ranges can be reduced by down-weighting them properly. Therefore, instead of comparing orbit and clock separately, user range accuracy should be investigated as usual. In this study, the quality of the estimated orbits and clocks is assessed by the repeatability of the station coordinates derived by PPP using those products.
Figure 6. Statistics of the overlap differences of the estimated receiver and satellite clocks. Satellite C10 is selected as the reference clock.
Precise Point Positioning
With these estimates of satellite orbits and clocks, PPP in static and kinematic mode are carried out for a user station that is not involved in the orbit and clock estimation, to demonstrate the accuracy of the Compass PPP service.
In the PPP processing, ionosphere-free phase and range are used with proper weight. Satellite orbits and clocks are fixed to the abovementioned estimates. Receiver clock is estimated epoch-wise, remaining tropospheric delay after an a priori model correction is parameterized with a random-walk process. Carrier-phase ambiguities are estimated but not fixed to integer. Station coordinates are estimated according to the positioning mode: as determined parameters for static mode or as epoch-wise independent parameters for kinematic mode.
Data from days 123 to 135 at station CHDU in Chengdu, which is not involved in the orbit and clock determination, is selected as user station in the PPP processing. The estimated station coordinates and ZTD are compared to those estimated with GPS data, respectively.
Static PPP. In the static test, PPP is performed with session length of 2 hours, 6 hours, 12 hours, and 24 hours. Figure 7 and Table 4 show the statistics of the position differences of the static solutions with various session lengths over days 123 to 125.
The accuracy of the PPP-derived positions with 2 hours data is about 5 cm, 3 cm, and 10 cm in east, north, and vertical, compared to the GPS daily solution. Accuracy improves with session lengths. If data of 6 hours or longer are involved in the processing, position accuracy is about 1 cm in east and north and 4 cm in vertical. From Table 4, the accuracy is improved to a few millimeters in horizontal and 2 cm in vertical with observations of 12 to 24 hours. The larger RMS in vertical might be caused by the different PCO and PCV of the receiver antenna for GPS and Compass, which is not yet available.
Figure 7. Position differences of static PPP solutions with session length of 2 hours, 6 hours, 12 hours, and 24 hours compared to the estimates using daily GPS data for station CHDU.Table 4. RMS of PPP position with different session length.
Kinematic PPP. Kinematic PPP is applied to the CHDU station using the same orbit and clock products as for the static positioning for days 123 to 125 in 2012.
The result of day 125 is presented here as example. The positions are estimated by means of the sequential least-squares adjustment with a very loose constraint of 1 meter to positions at two adjacent epochs. The result estimated with backward smoothing is shown in Figure 8. The differences are related to the daily Compass static solution. The bias and STD of the differences in east, north, and vertical are listed in Table 5. The bias is about 16 mm, 13 mm, and 1 mm, and the STD is 10 mm, 14 mm and 55 mm, in east, north, and vertical, respectively.
Figure 8. Position differences of the kinematic PPP and the daily static solution, and number of satellites observed.Table 5. Statistics of the position differences of the kinematic PPP in post-processing mode and the daily solution. (m)
Compass-Derived ZTD. ZTD is a very important product that can be derived from GNSS observations besides the precise orbits and clocks and positions. It plays a crucial role in meteorological study and weather forecasting.
ZTD at the CHDU station is estimated as a stochastic process with a power density of 5 mm √hour by fixing satellite orbits, clocks, and station coordinates to their precisely estimated values, as is usually done for GPS data.
The same processing procedure is also applied to the GPS data collected at the station, but with IGS final orbits and clocks. The ZTD time series derived independently from Compass and GPS observations over days 123 to 125 in 2012 and their differences are shown on Figure 9.
Figure 9. Comparison of ZTD derived independently from GPS and COMPASS observations. The offset of the two time series is about -14 mm (GPS – COMPASS) and the STD is about 5 mm.
Obviously, the disagreement is mainly caused by Compass, because GPS-derived ZTD is confirmed of a much better quality by observations from other techniques. However, this disagreement could be reduced by applying corrected PCO and PCV corrections of the receiver antennas, and of course it will be significantly improved with more satellites in operation.
Simulated Real-Time PPP Service
Global real-time PPP service promises to be a very precise positioning service system. Hence we tried to investigate the capability of a Compass real-time PPP service by implementing a simulated real-time service system and testing with the available data set.
We used estimates of a three-day solution as a basis to predict the orbits of the next 12 hours. The predicted orbits are compared with the estimated ones from the three-day solution. The statistics of the predicted orbit differences for the first 12 hours on day 125 in 2012 are shown on Figure 10.
From Figure 10, GEOs and IGSOs have very similar STDs of about 30 cm on average. Thus, the significantly large RMS, up to 6 meters for C04 and C05, implies large constant difference in this direction. The large constant shift in the along-track direction is a major problem of the current Compass precise orbit determination. Fortunately, this constant bias does not affect the positioning quality very much, because in a regional system the effects of such bias on observations are very similar.
Figure 10. RMS (left) and STD (right) of the differences between predicted and estimated orbits.
With the predicted orbit hold fixed, satellite clocks are estimated epoch-by-epoch with fixed station coordinates. The estimated clocks are compared with the clocks of the three-day solution, and they agree within 0.5 ns in STD. As the separated comparison of orbits and clocks usually does not tell the truth of the accuracy of the real-time positioning service, simulated real-time positioning using the estimated orbits and clocks is performed to reveal the capability of Compass real-time positioning service.
Figure 11 presents the position differences of the simulated real-time PPP service and the ground truth from the static daily solution. Comparing the real-time PPP result in Figure 11 and the post-processing result in Figure 8, a convergence time of about a half-hour is needed for real-time PPP to get positions of 10-cm accuracy. Afterward, the accuracy stays within ±20 cm and gets better with time. The performance is very similar to that of GPS because at least six satellites were observed and on average seven satellites are involved in the positioning. No predicted orbit for C01 is available due to its maneuver on the day before. Comparing the constellation in the study and that planned for the regional system, there are still one GEO and four MEOs to be deployed in the operational regional system. Therefore, with the full constellation, accuracy of 1 decimeter or even of cm-level is achievable for the real-time precise positioning service using Compass only.
Figure 11. Position differences of the simulated real-time PPP and the static daily PPP. The number of observed satellites is also plotted.
Summary
The three-day precise orbit and clock estimation shows an orbit accuracy, measured by overlap 3D-RMS, of better than 288 cm for GEOs and 21 cm for IGSOs, and the accuracy of satellite clocks of 0.23 ns in STD and 0.56 in RMS. The largest orbit difference occurs in along-track direction which is almost a constant shift, while differences in the others are rather small.
The static PPP shows an accuracy of about 5 cm, 3 cm, and 10 cm in east, north, and vertical with two hours observations. With six hours or longer data, accuracy can reach to 1 cm in horizontal and better than 4 cm in vertical. The post-mission kinematic PPP can provide position accuracy of 2 cm, 2 cm, and 5 cm in east, north, and vertical. The high quality of PPP results suggests that the orbit biases, especially the large constant bias in along-track, can be compensated by the estimated satellite clocks and/or absorbed by ambiguity parameters due to the almost unchanged geometry for GEOs.
The simulated real-time PPP service also confirms that real-time positioning services of accuracy at 1 decimeter-level and even cm–level is achievable with the Compass constellation of only nine satellites. The accuracy will improve with completion of the regional system.
This is a preliminary achievement, accomplished in a short time. We look forward to results from other colleagues for comparison. Further studies will be conducted to validate new strategies for improving accuracy, reliability, and availability. We are also working on the integrated processing of data from Compass and other GNSSs. We expect that more Compass data, especially real-time data, can be made available for future investigation.
UA240 OEM card made by Unicore company and used in Compass reference stations.
Acknowledgments
We thank the GNSS research center at Wuhan University and the Compass authorities for making the data available for this study.
The material in this article was first presented at the ION-GNSS 2012 conference.
Maorong Ge received his Ph.D. in geodesy at Wuhan University, China. He is now a senior scientist and head of the GNSS real-time software group at the German Research Centre for Geosciences (GFZ Potsdam).
Hongping Zhang is an associate professor of the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University, and holds a Ph.D. in GNSS applications from Shanghai Astronomical Observatory. He designed the processing system of ionospheric modeling and prediction for the Compass system.
Xiaolin Jia is a senior engineer at Xian Research Institute of Surveying and Mapping. He received his Ph.D. from the Surveying and Mapping College of Zhengzhou Information Engineering University.
Shuli Song is an associate research fellow. She obtained her Ph.D. from the Shanghai Astronomical Observatory, Chinese Academy of sciences.
Jens Wickert obtained his doctor’s degree from Karl-Franzens-University Graz in geophysics/meteorology. He is acting head of the GPS/Galileo Earth Observation section at the German Research Center for Geosciences GFZ at Potsdam.
Collaborative Navigation in Transitional Environments
By Dorota A. Grejner-Brzezinska, J.N. (Nikki) Markiel, Charles K. Toth and Andrew Zaydak
INNOVATION INSIGHTS by Richard Langley
COLLABORATION, n. /kəˌlæbəˈreɪʃən/, n. of action. United labour, co-operation; esp. in literary, artistic, or scientific work — according to the Oxford English Dictionary. Collaboration is something we all practice, knowingly or unknowingly, even in our everyday lives. It generally results in a more productive outcome than acting individually. In scientific and engineering circles, collaboration in research is extremely common with most published papers having multiple authors, for example.
The term collaboration can be applied not only to the endeavors of human beings or other living creatures but also to inanimate objects, too. Researchers have developed systems of miniaturized robots and unmanned vehicles that operate collaboratively to complete a task. These platforms must navigate as part of their functions and this navigation can often be made more continuous and accurate if each individual platform navigates collaboratively in the group rather than autonomously. This is typically achieved by exchanging sensor measurements by some kind of short-range wireless technology such as Wi-Fi, ultra-wide band, or ZigBee, a suite of communication protocols for small, low-power digital radios based on an Institute of Electrical and Electronics Engineers’ standard for personal area networks.
A wide variety of navigation sensors can be implemented for collaborative navigation depending on whether the system is designed by outdoor use, for use inside buildings, or for operations in a wide variety of environments. In addition to GPS and other global navigation satellite systems, inertial measurement units, terrestrial radio-based navigation systems, laser and acoustic ranging, and image-based systems can be used.
In this month’s article, a team of researchers at The Ohio State University discusses a system under development for collaborative navigation in transitional environments — environments in which GPS alone is insufficient for continuous and accurate navigation. Their prototype system involves a land-based deployment vehicle and a human operator carrying a personal navigator sensor assembly, which initially navigate together before the personal navigator transitions to an indoor environment. This system will have multiple applications including helping first responders to emergencies. Read on.
“Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. To contact him, see the “Contributing Editors” section on page 6.
Collaborative navigation is an emerging field where a group of users navigates together by exchanging navigation and inter-user ranging information. This concept has been considered a viable alternative for GPS-challenged environments. However, most of the developed systems and approaches are based on fixed types and numbers of sensors per user or platform (restricted in sensor configuration) that eventually leads to a limitation in navigation capability, particularly in mixed or transition environments.
As an example of an applicable scenario, consider an emergency crew navigating initially in a deployment vehicle, and, when subsequently dispatched, continuing in collaborative mode, referring to the navigation solution of the other users and vehicles. This approach is designed to assure continuous navigation solution of distributed agents in transition environments, such as moving between open areas, partially obstructed areas, and indoors when different types of users need to maintain high-accuracy navigation capability in relative and absolute terms.
At The Ohio State University (OSU), we have developed systems that use multiple sensors and communications technologies to investigate, experimentally, the viability and performance attributes of such collaborative navigation. For our experiments, two platforms, a land-based deployment vehicle and a human operator carrying a personal navigator (PN) sensor assembly, initially navigate together before the PN transitions to the indoor environment.
In the article, we describe the concept of collaborative navigation, briefly describe the systems we have developed and the algorithms used, and report on the results of some of our tests. The focus of the study being reported here is on the environment-to-environment transition and indoor navigation based on 3D sensor imagery, initially in post-processing mode with a plan to transition to real time.
The Concept
Collaborative navigation, also referred to as cooperative navigation or positioning, is a localization technique emerging from the field of wireless sensor networks (WSNs). Typically, the nodes in a WSN can communicate with each other using wireless communications technology based on standards, such as Zigbee/IEEE 802.15.4. The communication signals in a WSN are used to derive the inter-nodal distances across the network. Then, the collaborative navigation solution is formed by integrating the inter-nodal range measurements among nodes (users) in the network using a centralized or decentralized Kalman filter, or a least-squares-based approach.
A paradigm shift from single to multi-sensor to multi-platform navigation is illustrated conceptually in Figure 1. While conventional sensor integration and integrated sensor systems are commonplace in navigation, sensor networks of integrated sensor systems are a relatively new development in navigation. Figure 2 illustrates the concept of collaborative navigation with emphasis on transitions between varying environments. In actual applications, example networks include those formed by soldiers, emergency crews, and formations of robots or unmanned vehicles, with the primary objective of achieving a sustained level of sufficient navigation accuracy in GPS-denied environments and assuring seamless transition among sensors, platforms, and environments.
Figure 1. Paradigm shift in sensor integration concept for navigation.Figure 2. Collaborative navigation and transition between varying environments.
Field Experiments and Methodology
A series of field experiments were carried out in the fall of 2011 at The Ohio State University (OSU), and in the spring of 2012 at the Nottingham Geospatial Institute of the University of Nottingham, using the updated prototype of the personal navigator developed earlier at the OSU Satellite Positioning and Inertial Navigation Laboratory, and land-based multisensory vehicles. Note that the PN prototype is not a miniaturized system, but rather a sensor assembly put together using commercial off-the-shelf components for demonstration purposes only.
The GPSVan (see Figure 3), the OSU mobile research navigation and mapping platform, and the recently upgraded OSU PN prototype (see Figure 4) jointly performed a variety of maneuvers, collecting data from multiple GPS receivers, inertial measurement units (IMUs), imaging sensors, and other devices. Parts of the collected data sets have been used for demonstrating the performance of navigation indoors and in the transition between environments, and it is this aspect of our experiments that will be discussed in the present article.
Figure 3. Land vehicle, OSU GPSVan.Figure 4. Personal navigator sensor assembly.
The GPSVan was equipped with navigation, tactical, and microelectromechanical systems (MEMS)-grade IMUs, installed in a two-level rigid metal cage, and the signals from two GPS antennas, mounted on the roof, were shared among multiple geodetic-grade dual-frequency GPS receivers. In addition, odometer data were logged, and optical imagery was acquired in some of the tests.
The first PN prototype system, developed in 2006–2007, used GPS, IMU, a digital barometer, a magnetometer compass, a human locomotion model, and 3D active imaging sensor, Flash LIDAR (an imaging light detection and ranging system using rapid laser pulses for subject illumination). Recently, the design was upgraded to include 2D/3D imaging sensors to provide better position and attitude estimates indoors, and to facilitate transition between outdoor and indoor environments. Consequently, the current configuration allows for better distance estimation among platforms, both indoors and outdoors, as well as improving the navigation and tracking performance in general.
The test area where data were acquired to support this study, shown in Figure 5, includes an open parking lot, moderately vegetated passages, a narrow alley between buildings, and a one-storey building for indoor navigation testing. The three typical scenarios used were:
1) Sensor/platform calibration: GPSVan and PN are connected and navigate together.
2) Both platforms moved closely together, that is, the GPSVan followed the PN’s trajectory.
3) Both platforms moved independently.
Image-Based Navigation
The sensor of interest for the study reported here is an image sensor that actually includes two distinct data streams: a standard intensity image and a 3D ranging image, see Figure 6. The unit consists primarily of a 640 × 480 pixel array of infrared detectors. The operational range of the sensor is 0.8–10 meters, with a range resolution of 1 centimeter at a 2-meter distance.
Figure 6. PN captured 3D image sequence from inside the building.
In this study, the image-based navigation (no IMU) was considered. To overcome this limitation, the intensity images acquired simultaneously with the range data by the unit were leveraged to provide crucial information. The two intensity images were processed utilizing the Scale Invariant Feature Transform (SIFT) algorithm to identify matching features between the pair of 2D intensity images.
The SIFT algorithm has been primarily applied to 1D and 2D imagery to date; the authors are not aware of any research efforts to apply SIFT to 3D datasets for the expressed purpose of positioning. Analysis at our laboratory supported well-published results regarding the exceptional performance of SIFT with respect to both repeatability and extraction of the feature content. The algorithm is remarkably robust to most image corruption schema, although white noise above 5 percent does appear to be the primary weakness of the algorithm. The algorithm suffers in three critical areas with respect to providing a 3D positioning solution. First, the algorithm is difficult to scale in terms of the number of descriptive points; that is, the algorithm quickly becomes computationally intractable for a large number (>5,000) of pixels. Secondly, the matching process is not unique; it is exceptionally feasible for the algorithm to match a single point in one image to multiple points in another image. Finally, since the algorithm loses spatial positioning capabilities to achieve the repeatability, the ability to utilize matching features for triangulation or trilateration becomes impaired. Owing to the noted issues, SIFT was not found to be a suitable methodology for real-time positioning based on 3D Flash LIDAR datasets.
Despite these drawbacks, the intensity images offer the only available sensor input beyond the 3D ranging image. As such, the SIFT methodology provides what we believe to be a “best in class” algorithmic approach for matching 2D intensity images. The necessity of leveraging the intensity images will be apparent shortly, as the schema for deriving platform position is explained.
The algorithm has been developed and implemented by the second author (see Further Reading for details). The algorithm utilizes eigenvector “signatures” for point features as a means to facilitate matching. The algorithm is comprised of four steps:
1) Segmentation
2) Coordinate frame transformation
3) Feature matching
4) Position and orientation determination.
The algorithm utilizes the eigenvector descriptors to merge points likely to belong to a surface and identify the pixels corresponding to transitions between surfaces. Utilizing an initial coarse estimate from the IMU system, the results from the previous frame are transformed into the current coordinate reference frame by means of a Random Sampling Consensus or RANSAC methodology. Matching of static transitional pixels is accomplished by comparing eigenvector “signatures” within a constrained search window. Once matching features are identified and determined to be static, the closed form quaternion solution is utilized to derive the position and orientation of the acquisition device, and the result updates the inertial system in the same manner as a GPS receiver within the common GPS/IMU integration. The algorithm is unique in that the threshold mechanisms at each step are derived from the data itself, rather than relying upon a-priori limits. Since the algorithm only utilizes transitional pixels for matching, a significant reduction in dimensionality is generally accomplished and facilitates implementation on larger data frames.
The key point in this overview is the need to provide coarse positioning information to the 3D matching algorithm to constrain the search space for matching eigenvector signatures. Since the IMU data were not available, the matching SIFT features from the intensity images were correlated with the associated range pixel measurements, and these range measurements were utilized in Horn’s Method (see Further Reading) to provide the coarse adjustment between consecutive range image frames. The 3D-range-matching algorithm described above then proceeds normally.
The use of SIFT to provide the initial matching between the images entails the acceptance of several critical issues, beyond the limitations previously discussed. First, since the SIFT algorithm is matching 2D features on the intensity image; there is no guarantee that the matched features represent static elements in the field of view. As an example, SIFT can easily “match” the logo on a shirt worn by a moving person; since the input data will include the position of non-static elements, the resulting coarse adjustment may possess very large biases (in position). If these biases are significant, constraining the search space may be infeasible, resulting in either the inability to generate eigenvector matches (worst case) or a longer search time (best case). Since the 3D-range-matching algorithm checks the two range images for consistency before the matching process begins, this can be largely mitigated in implementation. Secondly, the SIFT features are located with sub-pixel location, thus the correlation to the range pixel image will inherently possess an error of ± 1 pixel (row and column). The impact of this error is that range pixels utilized to facilitate the coarse adjustment may in fact not be correct; the correct range pixel to be matched may not be the one selected. This will result in larger errors during the initial (coarse) adjustment process. Third, the uncertainty of the coarse adjustment is not known, so a-priori estimates of the error ellipse must be made to establish the eigenvector search space. The size and extent of these error ellipses is not defined on-the-fly by the data, which reduces one of the key elements of the 3D matching algorithm. Fourth, the limited range of the image sensor results in a condition where intensity features have no associated range measurement (the feature is out of range for the range device). This reduces the effective use of SIFT features for coarse alignment. However, using the intensity images does demonstrate the ability of the 3D-range-matching algorithm to generically utilize coarse adjustment information and refine the result to provide a navigation solution.
Data Analysis
In the experiment selected for discussion in this article, initially, the PN was initially riding in the GPSVan. After completing several loops in the parking lot (the upper portion of Figure 5), the PN then departed the vehicle and entered the building (see Figure 7), exited the facility, completed a trajectory around the second building (denoted as “mixed area” in Figure 5), and then returned to the parking lot.
Figure 7. Building used as part of the test trajectory for indoor and transition environment testing; yellow line: nominal personal navigator indoor trajectories; arrows: direction of personal navigator motion inside the building; insert: reconstructed trajectory section, based on 3D image-based navigation.
While minor GPS outages can occur under the canopy of trees, the critical portion of the trajectory is the portion occurring inside the building since the PN platform will be unable to access the GPS signal during this portion of the trajectory. Our efforts are therefore focused on providing alternative methods for positioning to bridge this critical gap.
Utilizing the combined intensity images (for coarse adjustment via SIFT) and the 3D ranging data, a trajectory was derived for travel inside the building at the OSU Supercomputing Facility. There is a finite interval between exiting the building and recovery of GPS signal lock during which the range acquisition was not available; thus the total extent of travel distance during GPS signal outage is not precisely identical to the travel distance where 3D range solutions were utilized for positioning. We estimate the distance from recovery of GPS signals to the last known 3D ranging-derived position to be approximately 3 meters. Based upon this estimate, the travel distance inside the building should be approximately 53.5 meters (forward), 9.5 meters (right), and 0.75 meters (vertical). Based upon these estimates, the total misclosure based upon 3D range-derived positions is provided in Table 1. The asterisk in the third row indicates the estimated nature of these values.
Table 1. Approximate positional results for the OSU Supercomputing Facility trajectory.
The average positional uncertainty reflects the relative, frame-to-frame error reported by the algorithm during the indoor trajectory. This includes both IMU and 3D ranging solutions. The primary reason for the rather large misclosure in the forward and vertical directions is the result of three distinct issues. First, the image ranging sensor has a limited range; during certain portions of the trajectory the sensor is nearly “blind” due to lack of measurable features within the range. During this period, the algorithm must default to the IMU data, which is known to be suspect, as previously discussed. Secondly, the correlation between SIFT features and range measurement pixels can induce errors, as discussed above. Third, the 3D range positions and the IMU data were not integrated in this demonstration; the range positions were used to substitute for the lost GPS signals and the IMU was drifting. Resolving this final issue would, at a minimum, reduce the IMU drift error and improve the overall solution.
A follow-up study conducted at a different facility was completed using the same platform and methodology. In this study, a complete traverse was completed indoors forming a “box” or square trajectory, which returned to the original entrance point. A plot of the trajectory results is provided in Figure 8. The misclosure is less than four meters with respect to both the forward (z) and right (x) directions. While similar issues exist with IMU drift (owing to lack of tight integration with the ranging data), a number of problems between the SIFT feature/range pixel correlation portion of the algorithm are evident; note the large “clumps’ of data points, where the algorithm struggles to reconcile the motions reported by the coarse (SIFT-derived) position and the range-derived position.
As demonstrated in this paper, the determination of position based upon 3D range measurements can be seen to have particular potential benefit for the problem of navigation during periods of operation in GPS-denied environments. The experiment demonstrates several salient points of use in our ongoing research activities. First, the effective measurement range of the sensor is paramount; the trivial (but essential) need to acquire data is critical to success. A major problem was the presence of matching SIFT features but no corresponding range measurement. Second, orientation information is just as critical as position; the lack of this information significantly extended the time required to match features (via eigenvector signatures). Third, there is a critical need for the sensor to scan not only forward (along the trajectory) but also right/left and up/down. Obtaining features in all axes would support efforts to minimize IMU drift, particularly in the vertical. Alternatively, a wider field of view could conceivably accomplish the same objective. Finally, the algorithm was not fully integrated as a substitute for GPS positioning and the IMU was free to drift. Since the 3D ranging algorithm cannot guarantee a solution for all epochs, accurate IMU positioning is critical to bridge these outages. Fully integrating the 3D ranging solution with a GPS/IMU/3D schema would significantly reduce positional errors and misclosure.
Our study indicates that leveraging 3D ranging images to achieve indoor relative (frame-to-frame) positioning shows great promise. The utilization of SIFT to match intensity images was an unfortunate necessity dictated by data availability; the method is technically feasible but our efforts would suggest there are significant drawbacks to this application, both in terms of efficiency and positional accuracy. It would be better to use IMU data with orientation solutions to derive the best possible solution. Our next step is the full integration within the IMU to enable 3D ranging solutions to update the ongoing trajectory, which we believe will reduce the misclosure and provide enhanced solutions supporting autonomous (or semi-autonomous) navigation.
Acknowledgments
This article is based on the paper “Cooperative Navigation in Transitional Environments,” presented at presented at PLANS 2012, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium held in Myrtle Beach, South Carolina, April 23–26, 2012.
Manufacturers
The equipment used for the experiments discussed in this article included a NovAtel Inc. SPAN system consisting of a NovAtel OEMV GPScard, a Honeywell International Inc. HG1700 Ring Laser Gyro IMU, a Microsoft Xbox Kinect 3D imaging sensor, and a Casio Computer Co., Ltd. Exilim EX-H20G Hybrid-GPS digital camera.
DOROTA GREJNER-BRZEZINSKA is a professor and leads the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at OSU, where she received her M.S. and Ph.D. degrees in geodetic science.
J.N. (NIKKI) MARKIEL is a lead geophysical scientist at the National Geospatial-Intelligence Agency. She obtained her Ph.D. in geodetic engineering at OSU.
CHARLES TOTH is a senior research scientist at OSU’s Center for Mapping. He received a Ph.D. in electrical engineering and geoinformation sciences from the Technical University of Budapest, Hungary.
ANDREW ZAYDAK is a Ph.D. candidate in geodetic engineering at OSU.
FURTHER READING
◾ The Concept of Collaborative Navigation
“The Network-based Collaborative Navigation for Land Vehicle Applications in GPS-denied Environment” by J-K. Lee, D.A. Grejner-Brzezinska and C. Toth in the Royal Institute of Navigation Journal of Navigation; in press.
“Positioning and Navigation in GPS-challenged Environments: Cooperative Navigation Concept” by D.A. Grejner-Brzezinska, J-K. Lee and C. K. Toth, presented at FIG Working Week 2011, Marrakech, Morocco, May 18-22, 2011.
“Network-Based Collaborative Navigation for Ground-Based Users in GPS-Challenged Environments” by J-K. Lee, D. Grejner-Brzezinska, and C.K. Toth in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21-24, 2010, pp. 3380-3387.
◾ Sensors Supporting Collaborative Navigation
“Challenged Positions: Dynamic Sensor Network, Distributed GPS Aperture, and Inter-nodal Ranging Signals” by D.A. Grejner-Brzezinska, C.K. Toth, J. Gupta, L. Lei, and X. Wang in GPS World, Vol. 21, No. 9, September 2010, pp. 35-42.
“Positioning in GPS-challenged Environments: Dynamic Sensor Network with Distributed GPS Aperture and Inter-nodal Ranging Signals” by D.A. Grejner-Brzezinska, C. K. Toth, L. Li, J. Park, X. Wang, H. Sun, I.J. Gupta, K. Huggins and Y. F. Zheng (2009): in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22-25, 2009, pp. 111–123.
“Separation of Static and Non-Static Features from Three Dimensional Datasets: Supporting Positional Location in GPS Challenged Environments – An Update” by J.N. Markiel, D. Grejner-Brzezinska, and C. Toth in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25-28, 2007, pp. 60-69.
◾ Personal Navigation
“Personal Navigation: Extending Mobile Mapping Technologies Into Indoor Environments” by D. Grejner-Brzezinska, C. Toth, J. Markiel, and S. Moafipoor in Boletim De Ciencias Geodesicas, Vol. 15, No. 5, 2010, pp. 790-806.
“A Fuzzy Dead Reckoning Algorithm for a Personal Navigator” by S. Moafipoor, D.A. Grejner-Brzezinska, and C.K. Toth, in Navigation, Vol. 55, No. 4, Winter 2008, pp. 241-254.
“Quality Assurance/Quality Control Analysis of Dead Reckoning Parameters in a Personal Navigator” by S. Moafipoor, D. Grejner-Brzezinska, C.K. Toth, and C. Rizos in Location Based Services & TeleCartography II: From Sensor Fusion to Context Models, G. Gartner and K. Rehrl (Eds.), Lecture Notes in Geoinformation & Cartography, Springer-Verlag, Berlin and Heidelberg, 2008, pp. 333-351.
“Pedestrian Tracking and Navigation Using Adaptive Knowledge System Based on Neural Networks and Fuzzy Logic” by S. Moafipoor, D. Grejner-Brzezinska, C.K. Toth, and C. Rizos in Journal of Applied Geodesy, Vol. 1, No. 3, 2008, pp. 111-123.
◾ Horn’s Method
“Closed-form Solution of Absolute Orientation Using Unit Quaternions” by B.K.P. Horn in Journal of the Optical Society of America, Vol. 4, No. 4, April 1987, p. 629-642.
October 12: A Soyuz launcher carrying two Galileo In-Orbit Validation (IOV) satellites deployed its twins into orbit within 4 hours after take-off, at close to 23 200 km altitude. They join two earlier IOV spacecraft launched in October 2011. Once all four are operational in space, they will provide the minimum number of satellites required for navigational fixes — enabling system validation testing when all are visible in the sky.
The satellites were built by a consortium led by the Astrium division of EADS, which produced the platforms and has responsibility for the payloads, while Thales Alenia Space handled assembly and testing.
October 11: The L5 transmitter aboard GPS Block IIF-3 satellite SVN65/PRN24 was switched on, transmitting the civilian safety-of-life GPS signal, designed to meet demanding requirements for safety-of-life transportation and other high-performance applications.
A day earlier, SVN65 began transmitting L1 and L2 signals as PRN24 on October 8. A number of stations of the International GNSS Service are tracking the satellite. As of press date for this magazine (October 25) the satellite is included in broadcast almanacs although it is set unhealthy and will continue to be so until satellite commissioning is completed. The satellite is drifting towards its designated orbital position of Slot 1 in Plane A.
The launch of the GPS Block IIF-3 satellite took place as scheduled October 4, aboard a United Launch Alliance Delta IV rocket from Cape Canaveral, Florida.
October 1: The two BeiDou-2/Compass satellites launched on September 18 reached their circular medium-Earth orbits and started transmitting navigation signals. Several stations participating in the International GNSS Service’s Multi-GNSS Experiment as well as some in the Cooperative Network for GNSS Observation started tracking the satellites on September 26.
Meanwhile, China is in the final stage of preparations for Compass G6 (G2R) satellite launch, scheduled to occur on October 25 from Xichang Launch Center.
October 3: The Indian Space Research Organization announced that the orbit-raising maneuvers of GSAT-10 satellite have been successfully completed from ISRO’s Master Control Facility. The maneuvers placed the GSAT-10, launched September 30, in an orbit with 35,000-kilometer high orbit, with an orbit period of 23 hours 50 minutes, and a designated location of 83 degree East. GSAT-10 contains a payload to support the Indian GPS and GEO Augmented Navigation (GAGAN) satellite-based augmentation system. The satellite will likely use PRN code 128.
Looking forward:
November 2: A Russian rocket carrying a Luch data-relay satellite with a payload to service the the Russian satnav system is due to launch on this day, postponed from earlier dates in August and October. The second of a set of three geostationary satellites launched to reactivate Roscosmos’s Luch Multifunctional Space Relay System, it will also carry transponders for the System for Differential Correction and Monitoring (SDCM), Russia’s satellite-based augmentation system. The transponders will broadcast GNSS corrections on the standard GPS L1 frequency using C/A PRN codes assigned by DoD’s Global Positioning Systems Directorate. According to the most recent announcement, it will be positioned at 16 degrees West longitude, joining Luch-5A, already in an orbital slot at 95 degrees East longitude.
Protecting GNSS Presentation at ION and INTERGEO
How to test receivers, how to monitor interference, and how to report interference formed the focus of “Protecting GNSS,” a presentation given at ION-GNSS in September and at the INTERGEO exhibition in Germany in October. GPS World hopes to present a video of the talk and its presentation slides at env-gpsworld-integration.kinsta.cloud/video in the near future.
In his talk, CEO Javad Ashjaee of JAVAD GNSS discusses the differences between out-of-band interference (“easy to deal with”) and in-band interference (“more difficult to deal with”). For the latter case, he offers a 64th-order adaptive filter for narrow-band carrier-wave (CW) interference, known as J-Shield, that is incorporated in current JAVAD GNSS receivers, for example the Triumph-VS and Victor-VS. This feature implements, he states, embedded real-time monitoring at the touch of two buttons on the receiver.
Users can then view, in the radio-frequency analysis stage, five different aspects of interference detection and monitoring:
◾ Spectrum shape
◾ Average automatic gain control (AGC)
◾ AGC variations
◾ carrier-to-noise (C/N0) losses
◾ Real-time cycle slips.
In subsequent digital analysis, after digital processing of the signals, Ashjaee showed interference detected by the company’s receivers operating from its San Jose office (see slide, interference with L2) and Moscow office, regarding both GPS and GLONASS signals.
Reporting. TRIUMPH-VS and Victor-VS can send interference reports to FTP sites and authorized persons can view them via browsers (computers, iPhones, and so on). The receivers can also email reports to intended people.
Ashjaee advocated for GNSS receivers in all reference stations to have such interference monitoring and reporting features. In this way, users could monitor interference in their area before performing tasks, just as pilots check the weather before take-off.
China, Europe to Negotiate Spectrum
The European Union (EU) and China will reportedly meet in December in Paris to discuss overlapping radio frequencies both plan to use for their future encrypted government/military satellite navigation services.
The meeting will be conducted under what the Joint Statement on Space Technology Cooperation specifies as the ITU Framework. ITU is the International Telecommunication Union of Geneva, a United Nations affiliate that regulates satellite orbital slots and frequencies.
The statement was signed as an annex to a broader EU-China summit held September 20 in Brussels. The two sides continue collaboration on satellite navigation despite the signal conflict, which has been a subject of debate for at least two years.
The 27-nation EU and China have agreed to continue the China-Europe GNSS Technology Training and Cooperation Center.
Contract for 37 New GLONASS Birds
A federal target program, approved by the Russian Government, has provided measures to maintain and develop the GLONASS system. The Reshetnev Company from 2012 to 2020 will manufacture 15 GLONASS-M satellites and 22 GLONASS-K. The work in this direction is taking place at ISS at full speed. Now the company is making space apparatus GLONASS-M No. 50 (likely to be known as 750 once launched) and has signed contracts with related enterprises for the supply of equipment for a few more satellites in this series. ISS has already completed the manufacture of satellites GLONASS-M No. 47, No. 48, and No. 49. Routine tests confirmed compliance characteristics of the design and with operational documentation. The space vehicles have been put in the assembly shop for safekeeping. ISS has sent a next-generation navigation satellite GLONASS-K No. 12L to the spaceport. A decision on the launch date of the navigation satellites will be made by Roscosmos after an analysis of the state of the GLONASS constellation.
Leadership Awards, Directions 2013 Next Month
The following individuals received GPS World 2012 Leadership Awards in September in Nashville, Tennessee. The magazine’s Leadership Dinner and awards were sponsored by Lockheed Martin and Deimos Space.
Satellites: Martin Unwin, Surrey Satellite Technology Limited. One of the driving forces behind the GIOVE-A satellite (recently retired) and the Galileo IOV satellites.
Signals: Todd Humphrey, Radionavigation Laboratory , University of Texas at Austin. Leader of several seminal studies on spoofing and jamming; testified this summer before Congress on the subject.
Services: Waldemar Kunysz, NextNav LLC. Work on WAPS (Widea Area Positioning System) design and implementation in the continental USA. He spent the previous 16 years with NovAtel on various research projects and novel antenna designs.
Products: Robert Lutwak, Symmetricom. Practical advances to overcome the intrinsic physical barriers to affordable chip-scale atomic clocks, enabling precision time and time transfer in mobile GNSS and communications systems.
Remarks by the award winners on the future of GNSS will appear as Directions 2013 in December issue.
By J. Blake Bullock, Mahesh Chowdhary, Dimitri Rubin, Donald Leimer, Greg Turetzky, and Murray Jarvis
A new chip fuses input from several sensors, using the best combination at any given time to maximize coverage and accuracy while keeping power draw to a minimum. This produces continuous position availability in indoor environments, as demonstrated by performance measurements in real-world test environments.
Users of GPS receivers in smartphones and many other consumer electronic devices expect these devices to work in all environments, including dense urban canyons, parking garages, and indoors, enabling a wide range of location-based services such as mapping, search, tracking, and navigation. Recent advancements in assisted-GPS (A-GPS) technology have enabled improved positioning indoors, but GPS receivers are still not sensitive enough to determine position everywhere that users go.
Several consumer products now use GLONASS and assisted-GLONASS (A-GLONASS) measurements to improve coverage and accuracy of GPS receivers. We refer to such combo receivers as GNSS receivers here. GLONASS measurements have similar characteristics to GPS measurements in that they are subject to blockage and multipath. In dense urban canyons, GLONASS measurements help to improve availability and accuracy of a position solution. However,GLONASS provides little performance improvement indoors.
Various emerging technologies for indoor positioning use installed wireless transmitters as beacons for making measurements for positioning. Existing Wi-Fi access points (APs) can be used in this way to determine position when indoors. Other solutions include the emerging Bluetooth Smart transmitters, GSM, 3G, and other mobile phone transmitters, the NextNav network, and other dedicated beacons for indoor positioning. Each technology has advantages and disadvantages for use as an indoor solution, to be discussed here.
The SiRFstarV location chip with SiRFusion combines A-GPS and A-GLONASS advances with Wi-Fi positioning and dead reckoning using low-cost micro-electro-mechanical systems (MEMS) sensors. Smartphones, tablets, cameras, fitness products, and other consumer electronics are equipped with an increasing array of MEMS sensors including accelerometers, magnetometers, gyroscopes, and barometers. The SiRFstarV chip acts as a gateway to receive input from all available MEMS sensors so that the output signals can be combined with the GPS, GLONASS, and Wi-Fi measurements that give absolute position. The observations from all these sources are fused together using a Kalman Filter. Smart location management makes use of the best combination of sensors at any given time to maximize coverage and accuracy while keeping power draw to a minimum. This produces continuous position availability in indoor environments.
Target Performance and Use Cases
The last 10 years have seen great improvements in GPS positioning indoors, primarily driven by the mobile market and the FCC E911 directive to be able to locate mobile-phone users. Today, it is possible to locate a mobile phone indoors using A-GPS, advanced forward link trilateration (AFLT), or Wi-Fi positioning. Typically it takes several seconds to determine a fix indoors, and the accuracy is not as good as outside. It is also not feasible to get continuous position updates for use in tracking, fitness, or navigation systems.
Wi-Fi positioning has improved the availability of fixes indoors and also the time to get a fix. However, today AP positioning is based on surveys that have been done using GPS vehicles outside, so the determined positions tend also to be outside, even when the mobile device is indoors.
To reliably deliver indoor positioning, the positioning system must be able to:
◾ Determine position quickly — within a few seconds.
◾ Determine position accurately — within 5–10 meters, circular error probable (CEP) 50 percent.
◾ Determine position updates at 1 Hz.
◾ Preserve battery life.
Cameras have very different uses than handsets. Typically, a camera is off until the user is ready to take a picture or video. When a picture is taken, theposition can be recorded and used to geotag the image with the location, date, and time. For this use case, the positioning system needs to be able to determine position indoors quickly and with low power, but continuous updates at 1 Hz are not needed.
Fitness products use location for recording distance traveled, speed, elevation, calorie counting, and showing a track of running or cycling workouts. Users value good accuracy and a fast startup time when they are about to begin a workout. The positioning system needs to be able to determine position continuously, but not necessarily show the position updates in real time.
Battery life for a typical assest-tracking device is extremely important, as is the ability to locate the asset in any environment. Continuous position updates are not needed. A typical feature of asset-tracking systems is the ability to set a geofence boundary, used for generating alerts. The positioning system needs to determine position periodically and compare with the geofence. If the position is outside the geofence, an alert is sent to the user.
GNSS Positioning
Positioning algorithms on the SiRFstarV Quad-GNSS combine range measurements from all-in-view GPS, GLONASS, QZSS, and SBAS satellites. The chip is hardware-ready to enable Galileo and Compass measurements with a future software update. Immunity to interference, cross-correlation, and multipath impairments are provided to achieve very high sensitivity, which is critical for indoor positioning. Nevertheless, the utility of reception sensitivities below –165 dBm has been found to have limited value for all but static cases, due to the very long integration times required to make reliable measurements. Increasing the number of independent range measurements helps improve indoor positioning, and using multiple constellations is a key enabler to provide them.
The improvement in indoor positioning by using multiple constellations is similar to the improvement in urban canyon positioning, since the impairments are similar.
One significant difference is that multipath delays for indoor environments are typically much shorter, and conventional mitigation methods cannot be applied without a very wide RF bandwidth. The shorter delays therefore produce lower signal levels due to phase cancellations and pseudorange bias errors, which are recognized as multipath errors and reduced as part of the chip’s measurement processing. While the advantage of augmenting GPS measurements with GLONASS is typically 20 to 40 percent improvement in position accuracy in urban canyon environments, it shrinks to only 7 to 15 percent indoors. Even with GLONASS measurements, the position is frequently shown outside of the building.
Figure 1 shows the results of an indoor walk test with a SiRFstarV receiver using GPS and GLONASS. The test was done on multiple floors of a three-story commercial building. Table 1 shows a summary of the performance metrics as determined by stopping at benchmark locations during the test. Fixes are available nearly 97 percent of the time. The addition of GLONASS tracking increased the average number of satellite measurements from 7.3 to 9.9 and improved the horizontal and vertical accuracy by about 7 to 15 percent. The horizontal accuracy is about 11.5 meters, 50 percent CEP. However, more than half the fixes are shown outside of the building.
Table 1. Impact of GLONASS on indoor positioning.
This test had high availability, but many environments cannot provide GNSS signals with sufficient energy to obtain position fixes. While the use of multiple constellations improves the accuracy and availability of the GNSS fixes, additional position sources are needed to achieve suitable availability and accuracy for continuous indoor positioning.
MEMS Pedestrian Dead Reckoning
Pedestrian dead-reckoning (PDR) logic is realized using integration of MEMS sensors with the SiRFstarV GNSS receiver, which has a dedicated I2C port designed to interface with MEMS sensors. A data-acquisition task collects sensor data and performs low-level error checking, timing synchronization, and buffering of the data from various sensors. This data is sent periodically to the process where a sensor data handler prepares it for further processing.
Acceleration data is processed by the context (or user mode) detection algorithm to determine the dynamic state of the user (or receiver) in order to select appropriate position-determination algorithms and associated motion parameters used by these algorithms. The PDR algorithm is employed when the user mode is classified as walking, fast-walking, jogging, stationary, climbing/descending stairs, elevator, and escalator.
The generalized navigation equation can be written as
(1)
where vne is ground velocity in navigation frame, Cnb is direction cosine matrix relating body reference frame to navigation frame, f b is specific force, ωnenis turn rate of Earth, ωnenis body rate, andgnlis local gravity vector expressed in navigation frame. This equation (in navigation frame) relates the ground speed of an object to measured specific force and measured body rate. The generalized navigation equation, when integrated twice, transforms from the acceleration of the platform into position represented in North and East reference frame, results in Equation 2,
(2)
where, s(t) is displacement and ψ(t) is heading. In the case of pedestrian motion, velocity and heading can be assumed to be constant during the interval when a step is taken. With this assumption, the integral form of Equation 2 can be rewritten as a difference equation with piece-wise linear approximation.
(3)
This equation describes a method of dead reckoning (DR) that is based on step counting rather than integration of acceleration and angular rate. This PDR process consists of three important components: the previously known absolute position of the user at time t-1 (Et-1, Nt-1), the stride length or distance traveled by the user since time t-1 (), and the user’s heading (ψ) since time t-1. The coordinates (Et, Nt) of a new position with respect to a previously known position (Et-1, Nt-1) can be computed as shown in Equation 3. The position initialization of the PDR process can be accomplished using any or a combination of absolute positioning technologies such as GNSS, Wi-Fi, or GSM.
Performance of PDR algorithms is dependent on obtaining calibrated MEMS inertial sensor data continuously. Calibration of sensors is accomplished through collecting and processing sensor data for user motion of device in Earth’s gravity and magnetic field. Accelerometer and gyroscope calibration logic utilize the knowledge of device stationary condition. Magnetic sensor calibration logic requires that various axes of sensor are exposed to Earth’s magnetic field vector at the user location. With the given time and location estimate, the Earth’s magnetic field parameters are computed using the World Magnetic Model. Normal use of a mobile device would result in rotations in various Euler planes thereby applying Earth’s magnetic field to various axes of magnetic sensor. Earth’s magnetic field parameters are also used to detect occurrences of magnetic disturbances. Magnetic sensor measurements are de-weighted for the PDR process during such magnetic disturbances.
The essential logic components that affect the performance of PDR positioning system are: calibration of sensors, step detection, determination of walking direction, positioning fusion logic, and orientation of phone while walking. Typical phone users will have the phone in a pocket, in a belt clip, in a purse or bag, in their hands looking at it, or up to their ear in a conversation. The PDR algorithms need to be able to perform robustly in any of these orientations.
With PDR, an absolute position can be propagated as a user moves on foot. Due to the error growth characteristics of typical MEMS devices used in consumerelectronics, the estimated path deviates from the actual path as a function of distance traveled. The error growth is typically on the order of 10 percent of distance traveled, especially in the presence of magnetic disturbances. This level of error growth makes MEMS PDR unsuitable as the sole positioning solution when indoors. Periodic absolute positioning updates are required to correct the path and to allow additional calibration.
Wi-Fi Positioning
Opportunistic positioning using observed Wi-Fi signals is a well established method of absolute positioning in GNSS-denied environments. Off-the-shelf Wi-Fi access point hardware is not well suited to positioning using timing observations, therefore the chip under discussion uses observed signal strengths together with the broadcast unique identifiers (BSSIDs) as the basis for the Wi-Fi positioning sub-system. Signal strength information is by its nature asymmetric. A strong observation of a Wi-Fi AP indicates that one is near it, but it is not safe to infer from a weak observation that you are far away. This is because weak observations may be due to, for example, occlusion, fading, or antenna orientation. This means that the performance of Wi-Fi positioning varies considerably with location and time, especially in areas with many pedestrians.
There are several limitations to Wi-Fi positioning. The first is that since it is opportunistic, there is no guarantee of performance. Fortunately, AP density is typically highest in the areas where Wi-Fi positioning is most needed, namely, deep indoors and in dense urban areas. Secondly, there is no guarantee that APs will remain in the same locations. APs may be attached to mobile devices, or AP equipment may simply be moved. This leads to a requirement for the database of AP locations to be dynamically monitored and continuously improved. Lastly, the location of the APs is not known a priori, and hence there needs to be some independent means of locating the APs in order for them to be used for positioning. The CSR server implementation uses the other technologies present, namely GNSS and MEMS, to generate this information. This avoids the need to manually survey areas where Wi-Fi positioning coverage is required.
The chip supports Wi-Fi receive (sniffing) and positioning via scanning of the ISM band to detect any broadcast 802.11b Barker codes on any of the 14 channels. This process takes approximately 100 milliseconds/channel, producing a scan time of 300 milliseconds for the three primary channels, or a scan time of 1.4 seconds for a systematic scan of the entire band.
The usual configuration is for the SiRFstarV chip to be connected to the CSR Positioning Center (CPC) server via software running on a host processor in the device. On request, the CPC can then provide the device with all the APs known to be in the vicinity of the user. This data is sent as a sequence of spatially contiguous sets of APs in a tiled structure. The benefit of serving tiles to the user, rather than user’s position or only the APs instantaneously detected, is that the client device can subsequently operate independently with only occasional server contact. In fact, since the chip supports on-board storage of the AP tile information, it can also operate for extended periods without waking up the host, a feature useful for low-power geo-fencing and other location functions.
Another important aspect of the CPC is that is supports crowd-sourced learning of Wi-Fi APs. Client SiRFusion devices submit anonymous sets of Wi-Fi signal strength data and associated BSSIDs, together with contemporaneous GNSS and relative information from the MEMS devices. By collating all the information available in an area across users, the system is able to calculate the most likely locations for Wi-Fi APs and hence generate tiles available to provide to all users. Unlike crowd-sourced systems based on GNSS alone, CSR also uses relative data from MEMS PDR to extend the coverage area of the crowd-sourcing indoors. This produces better Wi-Fi positioning performance indoors.
Sensor Fusion
The GNSS, Wi-Fi, and MEMS PDR solutions offer varying levels of accuracy, coverage, and reliability. CSR has developed SiRFusion, a Kalman filter-based fusion engine in the SiRFstarV device, to combine all these location inputs. Sensor fusion is a critical component and does the job of fusing the multiple sources of position information to provide a single best estimate of position and confidence to the user. It takes as input absolute positions from GNSS and Wi-Fi and also any relative information derived from the MEMS PDR sub-system. Figure 2 illustrates the major components of SiRFusion.
Figure 2. Major components of SiRFusion.
To determine how to weight and smooth the different inputs, it is crucial that the individual input technologies provide reliable estimates of their confidence and correlation. As an example, we mentioned earlier that the quality of Wi-Fi positioning is variable and is best when strong APs are seen. A high quality Wi-Fi position, signified by a high confidence value, will cause the fusion filter to be strongly biased towards this positioning source. When the Wi-Fi position quality subsequently deteriorates, this is reflected in a lower position confidence, and hence the fusion filter down-weights Wi-Fi influence. In turn, this allows dominance of the MEMS PDR input until another sufficiently high-quality absolute position allows the filter to correct. The net effect of this behavior is that the MEMS bridges the position output smoothly between high-quality absolute position fixes and to a first approximation, any low-grade information is ignored. Another benefit is that individual Wi-Fi positions can be jumpy, because on an individual scan there is considerable variation in the audible APs and their signal strengths. Sensor fusion with MEMS PDR helps to smooth this out, providing a continuous trajectory and a more satisfying user experience.
Another job of the fusion engine is to transition smoothly from indoors where Wi-Fi and MEMS PDR dominate, to outdoors where GNSS dominates. This happens automatically in the fusion filter with the GNSS becoming increasingly dominant outdoors as GNSS confidence improves. Conversely, the Wi-Fi position accuracy will typically decrease outdoors and the dominant technology will therefore gradually dominate the solution. When technologies are not being used they can be switched off or placed in a maintenance mode to reduce unnecessary power consumption.
Performance Results
CSR has developed a demo platform with SiRFstarV and SiRFusion in a modified HTC Google Nexus One handset with Android. Figure 3 shows a modulewith the receiver and MEMS devices; the module is mounted inside the HTC phone shown in Figure 4. The data log includes PDR output, Wi-Fi positioning, GNSS positioning, and the combined sensor-fusion solution.
Figure 3. Module with MEMS devices.Figure 4. HTC Google Nexus test phone.
A series of tests were carried out in Tokyo Station in Tokyo, Japan. The tests shown here were all done on the B1F level in the shopping area adjacent to the station. This area is two levels below the tracks and is below ground. There are no windows, and there was no GNSS reception. The environment also has lots of magnetic anomalies due to tracks, trains, elevators, escalators, and many people in motion, which affects Wi-Fi signals. Each plot shows an indoor map superimposed on the Google Earth image of the area. The narrow aisles in the map are about 5 meters wide. The map is used for presenting results only; it was not used to do map-aiding or map-matching.
AP harvesting and learning was done in this area before the tests were conducted. In each test, the phone is turned on, and SiRFusion uses Wi-Fi measurements and data from the AP database to determine the initial position without any assistance from GNSS. In each case, the initial position was determined within 1–3 seconds.
In Figure 5, the route walked is shown by the straight green line, with the start point in the lower left corner. Wi-Fi positioning is shown in red, the yellow isthe MEMS PDR solution, and the blue shows the SiRFusion solution, which in this case is combining Wi-Fi and PDR. The Wi-Fi position is not available every second and at times has discontinuities of several meters. This is due to the signal variability as discussed previously. The PDR solution shows a gradual drift that is more than 25 meters off track in places. This is not an issue for SiRFusion, as only the relative positioning is used from the PDR output. The SiRFusion solution shows a smooth continuous output that has a maximum cross-track error of about 7 meters. Note that the error of the SiRFusion solution does not follow the PDR solution. The absolute positioning provided by the Wi-Fi fixes keeps the solution on track.
Figure 5. Tokyo Station test showing Wi-Fi (red squares), PDR (light green squares), and SiRFusion output (blue); straight green line shows true path followed.
Figure 6 introduces a test with several turns in the corridors. The path walked is marked by the red flags, and took just under six minutes. The fusion solution is shown in blue. The start point was in the lower left. The fusion solution was able to detect each of the turns made while walking. The shape of the path clearly follows the marked path walked. The largest deviation from the path was ~7 meters. Typically, the solution was within 5 meters of the path walked.
Figure 6. Tokyo Station test showing turns; red flgas mark actual path, blue is SiRFusion output.
Figure 7 shows another path through the corridors, this time just over seven minutes in duration. Again, the fusion solution shows each turn correctly and in this case, the maximum cross-track error is about 5 meters. Figure 8 shows the same path, but with the output from three separate walks shown in green. A cold start was done before each walk. The results agree closely, showing high repeatability between test runs.
Figure 7. Tokyo Station test showing turns; legend as per Figure 6.Figure 8. Tokyo Station test repeatability; light green shows three successive SiRFusion test runs.
To obtain a quantitative measure of the performance accuracy, the locations of several points in the Valley Fair Mall in Santa Clara, California, were determined. During several independent test runs in the mall, the tester went to each designated test point and indicated a marker in the log. The measured positions were compared with the determined positions to generate the performance statistics shown in Table 2. The cross-track error was 3.2 meters 50 percent CEP and 13.1 meters 95 percent CEP. These levels agree with the estimated results determined from the maps in the Tokyo tests.
Table 2. Accuracy test, Santa Clara Mall.
These tests show excellent results in availability, accuracy, stability, and repeatability. The availability is near 100 percent, with the only missing fixes being the first couple of seconds on startup. The position accuracy is sufficient to guide a user to the correct storefront, terminal, or track in a complicated indoor environment. The smooth continuous output can be used for voice guidance applications.
Applications
Continuous indoor positioning enables important consumer and commercial applications including indoor search, navigation, social networking, andadvertising on mobile devices, indoor geotagging on camera devices, indoor workout monitoring on fitness devices, and asset tracking.
Mobile Devices. Search, mapping, and navigation are popular uses for smartphones, tablets, and other mobile devices. These services are even more powerful when taken indoors in shopping centers, airports, train stations, and other public places. In a large shopping area, a consumer can search for the nearest store with items of interest and get walking directions to that store. He or she may receive a coupon or ad relevant to the store or item that they searched for. Business owners are interested in targeted mobile ads to help connect with interested shoppers.
Camera Devices. Location capability is emerging on camera devices for geotagging the location where a photo was taken so that it can be embedded with other meta-data in the image file. Geotagged photos can be easily shown on maps, sorted by location, and shared with others. Indoor positioning enables geotags to work inside as well as outside, completing the coverage availability.
Fitness Devices. Fitness watches and other workout tracking products use location to measure distance traveled, calories burned, steps taken, and plot workout tracks on maps. With indoor positioning, indoor workouts can also be included in consumers’ data analysis as they track a wider variety of workout types.
Machine-to-Machine and Asset Tracking. The benefits of indoor location extend the asset-tracking model from fleets of trucks and automobiles to include all types of valuable assets, from children to pets to merchandise and even data. It is valuable to provide an individual with their own location, but it is even more valuable to provide the location of objects that are somewhere else in an M2M application. The low-power, ubiquitous location capability of SiRFstarV and SiRFusion allows very small tags with months of battery life to be attached to virtually any object and in combination with an appropriate communication link (cellular, Wi-Fi or BLE) report that position to the CPC. From there, a cloud-based location service to carriers, retailers, malls, government agencies and others can add location to their product mix. This service can even be extended to provide data security so that sensitive corporate information could only be accessed by devices within an authorized area, and not in a public place such as an airport. By making ubiquitous location information available on almost any imaginable platform, the use cases are nearly limitless.
Conclusion
Sensor fusion algorithms have been developed and refined to address the problem of determining position indoors. Performance testing shows that the position availability approaches 100 percent, and accuracy exceeds 10 meters, 50 percent CEP. The fusion technology is suitable for integrating in a wide range of consumer and commercial devices. The solution uses existing wireless infrastructure and can be deployed around the world with no new equipment to install or surveying to perform. The self-learning capability adapts to changes in the signal environment.
Acknowledgments
Seiji Ishikawa and Shinya Ohno of CSR performed the testing in Tokyo Station and were instrumental in preparation and analysis.
ST Microelectronics provides the MEMS sensors used in much of CSR SiRFusion testing.
J. Blake Bullock was senior product manager responsible for CSR’s next generation of GNSS solutions. He has now transferred to Samsung System LSI Business and is responsible for GNSS and indoor positioning solutions. He holds a M.Sc. degree in geomatics engineering from the University of Calgary, an MBA from Arizona State University, and several patents in LBS and navigation.
Mahesh Chowdhary is senior director MEMS technology at CSR where he works on the integration of GPS, MEMS sensors, and wireless technologies. As founder and CTO of Acculeon, he pioneered the use of GPS and MEMS sensors in vehicle safety applications. He received his Ph.D. in Applied Science from The College of William and Mary, Williamsburg, Virginia.
Dimitri Rubin is senior director at CSR and is responsible for the development of the SiRFusion system. He has worked in the wireless communication field for 24 years.
Don Leimer is managing the GNSS Advanced Development group at CSR. Mr. Leimer has led and contributed to numerous commercial and military GNSS developments including GPS Phase I.
Greg Turetzky is senior director for location and technology strategy in CTO office at CSR. He has an M.S. in computer science from Johns Hopkins and holds a number of patents in GPS.
Murray Jarvis is a consultant research and development engineer at CSR. He holds a Ph.D. in physics and has worked on a variety of positioning technologies including GNSS, cellular and Wi-Fi.
It was thirty years ago today, Cheremisin taught the band to play. They’ve been going in and out of style, but they’re guaranteed to raise a smile. So may I introduce to you the constellation here for years, Vladimir Putin’s GLObal NAv Sat System!
While in our booth at INTERGEO in Hanover last month, I heard Andrey Kupriyanov say it was GLONASS’s 30th birthday today, that particular today being October 12. “First satellites launched,” he recalled.
“Then it is the 30th birthday of GNSS as well,” I replied. “First GPS, then GLONASS. One plus one equals two: GNSS.” Andrey Kupriyanov nodded agreement, then told me a bit about his involvement in the program back then.
After graduating from the Moscow State University of Geodesy and Cartography in 1972, he obtained a Ph.D. in geodetic astronomy, taught for a while, then worked in the U.S.S.R. Ministry of the Merchant Marine, taking part in the development, testing, and application of new operational equipment for mid-Earth orbit satellites.
We’re Vladimir Putin’s GLObal NAv Sat System, we hope that you enjoy our show. We’re Vladimir Putin’s GLObal NAv Sat System, sit back and let PNT flow.
GLONASS achieved full operational status with 24 satellites in 1995, a year after GPS hit that milestone. The constellation subsequently declined to six operational satellites in 2001.
Andrey Kuypriyanov kept busy, representing Ashtech, Magellan, and Thales Navigation in Russia, and participating in research involving GPS and GLONASS monitoring, interaction, and eventual interoperability.
A recovering economy early this century enabled Russia to invest significantly in satnav again. Renewed launches and new spacecraft designs with longer lifetimes restored the constellation to full operational capability, with worldwide availability and greater accuracy.
Vladimir Putin’s global, Vladimir Putin’s global, Vladimir Putin’s GLObal NAv Sat System!
Andrey Kupriyanov is no longer the young man he once was (who among us is, really?) but he stays involved as executive director of the GLONASS-GNSS Forum and as NovAtel’s regional manager for Russia and the Commonwealth of Independent States.
It’s wonderful to be here, it’s certainly a thrill. You’re such a lovely user group, we’d like to take you home with us, we’d love to take you home.
Andrey Kupriyanov Olkgovich is of course only one of many, many long-laboring soldiers in the international GNSS brigade: engineers who made devices, product managers who carried them forth to market, users who embraced them. But on this 30th birthday of GNSS — we’re only just now hitting our stride, entering our golden years — let’s give him, and all of us, a rousing chorus.
I don’t really want to stop the show, but I thought you might like to know, that the singer’s going to sing a song, and he wants you all to sing along. So let me introduce to you the one and only Kupriyanov, and Vladimir Putin’s GLObal NAv Sat System!
The U.S. Air Force is investing to improve the Global Positioning System (GPS) used worldwide for military and civilian purposes.
Between Sept. 28 and Oct. 1, the Air Force announced four new GPS contracts.
Three were in the $30 million range, including contracts to Rockwell Collins and L-3 Communications to test and engineer new GPS technology, while Raytheon was awarded just under $30 million to develop receiver cards for GPS systems. Honeywell International also received a $14 million contract for engineering services related to GPS.
Maintained by the Air Force, the GPS is used in everything from civilian car navigation to targeting for military weapon systems. The only competition for the American GPS is the Russian GLONASS system, although the European Union is currently developing its own system, nicknamed Galileo.
The contracts were announced days before the Oct. 4 launch that put the first new GPS satellite of 2012 into orbit. That satellite, a Boeing-designed GPS IFF, improves on navigational accuracy, provides a more secure military signal and has a longer design life than older satellite models. It should deploy fully in about three months.
The winners of GPS World’s2012 Leadership Awards will be featured in November webinar “The Future of GNSS Research & Development.” The webinar will be held Thursday, November 15, at 10 a.m. PDT / 1 p.m. ET / 5 p.m. GMT. Registration is free.
The winners are expected to discuss with moderator and editor-in-chief Alan Cameron their significant recent achievement in four fields, as well as the future directions of their research or significant research that they think should be undertaken by others in the GNSS community.
The invited speakers are:
Martin Unwin, Surrey Satellite Technology Limited. One of the driving forces behind the GIOVE-A satellite (recently retired) and the Galileo IOV satellites.
Todd Humphrey, Radionavigation Laboratory, University of Texas at Austin. Received the GPSW Signals Leadership Award. Leader of several seminal studies on spoofing and jamming; testified this summer before Congress on the subject.
Waldemar Kunysz, NextNav LLC. Received the GPSW Services Leadership Award for his work on WAPS (Widea Area Positioning System) design and implementation in the continental USA. He spent the previous 16 years with NovAtel on various research projects and novel antenna designs.
Robert Lutwak, Symmetricom. Received the GPSW Products Leadership Award for practical advances to overcome the intrinsic physical barriers to affordable chip-scale atomic clocks, enabling precision time and time transfer in mobile GNSS and communications systems.
A week after the dual liftoff from Kourou, French Guiana, the two latest Galileo satellites completed the critical Launch and Early Orbit Phase on October 19-20. The satellites are expected to reach their assigned orbits November 10 and 12.
The FM3 and FM4 satellites were handed over from the joint ESA/CNES Launch and Early Orbit Phase (LEOP) team in Toulouse, France, to the Galileo Control Centre, Oberpfaffenhofen, Germany, from where Spaceopal will manage the operations of the Galileo constellation.
Following liftoff at 18:15 GMT on October 12, the intensive LEOP activities began upon separation of the satellites from the Fregat upper stage of their Soyuz launcher, with the first signals being received from the pair almost four hours later, according to the European Space Agency.
The handovers took place at 06:00 GMT on October 19 for FM4 and at 18:10 GMT on October 20 for FM3. During the week, LEOP operations proceeded according to the planned sequence.
Three orbit manoeuvres were conducted for each satellite to start them on drift orbits towards their operational positions, where they are expected to arrive on November 10 (FM3) and November 12 (FM4) after a series of drift-stop and fine-positioning manoeuvres.
The satellites were configured into a secure mode shortly after handover. While underway to their final positions, they will also undergo a series of tests to confirm the performance of their subsystems before switching on the payload.
Butler National Corporation, which specializes in the aerospace sector of structural modification, maintenance, repair, and overhaul, announces issuance of the Supplemental Type Certificate (STC) for installation of the new Garmin GTN series of navigators that provide GPS, navigation, and communications. The installation is for GTN 750 navigators in the Learjet Models 35/35A/36/36A with the FC-200 autopilot and the Learjet Model 24.
The Garmin GTN series features intuitive touchscreen controls and a large-screen display that give Learjet pilots unprecedented access to high-resolution mapping, graphical flight planning, and geo-referenced charting, among many other features. The installation also features new GPS roll-steering that allows seamless navigation operations with turn anticipation and waypoint sequencing interfaced to the autopilot.
“This approval offers a significant and economical avionics upgrade for the Lear 30 series airplanes,” said Clark Stewart, president and CEO of Butler. “The STC for the new Garmin GTN series allows us to tap into a sizeable upgrade market for retrofit of flight management systems. The Garmin GTN upgrade provides significant functionality upgrades, including WAAS GPS approaches and roll-steering interface to the autopilot.”
“We have designed the installation to provide cost-effective options to meet the various Learjet operator requirements. We will be offering the GTN Learjet installations through our avionics facility Kings Avionics starting under $100,000,” commented Craig Stewart, Aerospace division president.
Butler National Corporation operates in the Aerospace and Services business segments. The Aerospace segment focuses on the manufacturing of support systems for commercial and military aircraft including the Butler National TSD for the Boeing 737 and 747 Classic aircraft, switching equipment for Boeing McDonnell Douglas Aircraft, weapon control systems for Boeing Helicopter, and performance enhancement structural modifications for Learjet, Cessna, Dassault, and Beechcraft business aircraft.
u-blox has acquired privately owned, Finnish-based Fastrax Oy, a company that specializes in a broad range of GNSS positioning and antenna modules. The company brings additional products to u-blox’ portfolio, including software GNSS solutions used for consumer and industrial applications, and advanced GNSS modules that include an integrated antenna.
“Over its 12 year history, Fastrax has established itself as a successful player in the global positioning markets worldwide,” said Thomas Seiler, u-blox CEO. “Their modules exploit the best features of four leading GNSS chip vendors and include advanced antenna modules. These products are an excellent complement to our existing portfolio, and will benefit from u-blox’ economy of scale in terms of our advanced R&D capabilities, semiconductor technologies, global sales channels, established supply chain, and high-volume manufacturing resources.”
“Combining our leading GNSS products and technologies will give our customers a more attractive choice, while streamlining our operations and lower costs,” said Fastrax CEO, Taneli Tuurnala. “This merger brings together two recognized, profitable GNSS technology leaders with broad market base; the sum of this acquisition is significantly larger than its parts.”
The acquisition entails purchase of 100% of the shares of Fastrax Oy at a price of 13.0 million Euros. Certain pay-out rules apply to Fastrax management members, u-blox said in a statement. The company expects revenue of about CHF 2 million and an EBIT of CHF 0.1 million for the remainder of 2012, and revenue of CHF 13 to 15 million with an accretive EBIT margin of 15-20% for 2013.
