Raytheon Company’s GPS OCX program is ready for the U.S. Air Force’s launch of the first modernized GPS satellite later this year.
Raytheon’s GPS Next-Generation Operational Control System, known as GPS OCX, is in its final software development phase. This phase focuses on increasing automation and building controls for both L1C, a civilian GPS signal aimed at increasing international access, and M-code, a military GPS signal with better anti-jam capability.
Once complete, the team will begin integration and testing to keep the program on track for full system delivery in June 2021.
The GPS Operational Control System’s launch and checkout system will control launch and early orbit operations and the on-orbit checkout of all GPS III satellites. (Image: Raytheon)
“Our team has two primary goals this year,” said Dave Wajsgras, president of Raytheon intelligence, information and services. “We will support the U.S. Air Force’s GPS III launch this fall and complete the software build for the full operational system by year’s end.”
GPS OCX is the enhanced ground control segment of a U.S. Air Force-led effort to modernize America’s GPS system. The program is implementing 100 percent of DODI 8500.2 “Defense in Depth” information assurance standards without waivers, giving it the highest level of cybersecurity protections of any DoD space system, Raytheon said.
For protection against future cyber threats, the system’s open architecture allows it to integrate new capabilities and signals as they become available.
Because GPS OCX can manage nearly twice the satellites of the current system, it will increase signal strength in hard-to-reach areas like dense cities and mountainous terrain.
Also, advanced automation will free crews to focus on mission-critical tasks such as updating satellite positions more often.
The U.S. Federal Railroad Administration (FRA) released a status update on its efforts to assist railroads in implementing positive train control systems (PTC), along with the railroads’ self-reported progress for the fourth quarter of 2017.
The FRA said in a press release that it is taking a proactive approach to ensure railroads acquire, install, test and fully implement certified PTC systems in time to meet the congressional interim deadline of Dec. 31.
“It is the railroads’ responsibility to meet the congressionally mandated PTC requirements,” said FRA Administrator Ronald L. Batory. “The FRA is committed to doing its part to ensure railroads and suppliers are working together to implement PTC systems.”
Between Jan. 2 and Feb. 14, FRA’s leadership hosted face-to-face meetings with executives from each of the 41 railroads subject to the statutory mandate to evaluate each railroad’s PTC status and learn what remaining steps each needs to take to have a PTC system fully implemented by the December deadline or, at a minimum, to meet the statutory criteria necessary to qualify for an alternative schedule.
The FRA is now meeting with PTC suppliers to learn more about their capacity to meet the high demands for railroads’ implementation of PTC systems in a timely manner.
PTC systems are designed to prevent certain train-to-train collisions, over-speed derailments, incursions into established work zone limits, and trains going to the wrong tracks because a switch was left in the wrong position.
All railroads subject to the statutory PTC implementation mandate must implement FRA-certified and interoperable PTC systems by the end of the year.
Under the Positive Train Control Enforcement and Implementation Act of 2015, however, Congress permits a railroad to request FRA’s approval of an “alternate schedule” with a deadline beyond Dec. 31, 2018, but no later than Dec. 31, 2020, for certain non-hardware, operational aspects of PTC system implementation.
The congressional mandate requires the FRA to approve a railroad’s alternative schedule with a deadline no later than Dec. 31, 2020, if a railroad submits a written request to FRA that demonstrates the railroad has met the statutory criteria set forth under 49 U.S.C. § 20157(a)(3)(B).
The fourth quarter data, current as of Dec. 31, 2017, shows PTC systems are in operation on approximately 56 percent of freight railroads’ route miles that are required to be governed by PTC systems — up from 45 percent last quarter and 16 percent on Dec. 31, 2016. Passenger railroads have made less progress, with PTC systems in operation on only 24% of required route miles, unchanged from the previous quarter.
The latest data confirms that railroads continue to make progress in installing PTC system hardware, with 15 railroads reporting they have completed installation of all hardware necessary for PTC system implementation and another 11 railroads reporting they have installed over 80% of PTC system hardware. In addition, all but three railroads report having acquired sufficient spectrum for their PTC system needs.
For more key implementation data for the fourth quarter, see the infographics here.
To view the public version of each railroad’s Quarterly PTC Progress Report (Form FRA F 6180.165, OMB Control No. 2130-0553) for Quarter 4 of 2017, visit each railroad’s PTC docket on https://www.regulations.gov/. Railroads’ PTC docket numbers are available at https://www.fra.dot.gov/Page/P0628.
The Airbus-operated SpaceDataHighway has begun regularly relaying data from the Sentinel-2A satellite, after the successful end of the commissioning period.
This marks the start of the SpaceDataHighway service using all four Copernicus Sentinel satellites and the beginning of a new era for space-based imagery users.
The first two sets of Earth-observing Copernicus Sentinels-1A and -1B and -2A and -2B are signed up to this service as SpaceDataHighway’s anchor customers under an agreement between the European Union and the European Space Agency (ESA) as owners of the Copernicus programme, and Airbus as the owner and commercial operator of SpaceDataHighway.
Since using the SpaceDataHighway, the Sentinel-1 constellation has increased the amount of data it produces by about 50%. The service is also able to bring operational added-value to Sentinel-1 users by greatly improving the data timeliness for observations outside Europe. This is an important asset for users, especially when it comes to the routine monitoring of remote areas in the domain of maritime applications or assessment of natural disasters and first line response for emergency.
The SpaceDataHighway is the world’s first “optical fibre in the sky” based on cutting-edge laser technology. It will be a unique system of satellites permanently fixed over a network of ground stations, with the first — EDRS-A — already in space.
Each day, it can relay up to 40 terabytes of data acquired by observation satellites, UAVs and manned aircraft, at a rate of 1.8 gigabits per second.
The relay satellites are designed to lock on to low-orbiting satellites via laser and collect their data as they travel thousands of kilometres below, scanning Earth. SpaceDataHighway then immediately sends the collected data down to Europe from its higher position hovering in geostationary orbit, acting as a go-between.
This process allows the lower satellites to continuously downlink the information they are gathering, instead of having to store it until they travel over their own ground station. That way, they can send down more data, more quickly.
The SpaceDataHighway is a public-private partnership between ESA and Airbus, with the laser terminals developed by Tesat-Spacecom and the DLR German Space Administration. EDRS-A, the first SpaceDataHighway relay satellite launched in January 2016, offers coverage from the American East Coast to India. A second satellite will be launched in 2018.
It will double the system’s capacity and extend the coverage and redundancy of the system. Airbus is willing to expand the SpaceDataHighway with a third node, EDRS-D, to be positioned over the Asia-Pacific region.
In this month’s column, we travel along the road of PPP development, examine its current status and look at where it might go in the near future
By Sunil Bisnath, John Aggrey, Garrett Seepersad and Maninder Gill
Innovation Insights with Richard Langley
PPP. It’s one of the many acronyms (or initialisms, if you prefer) associated with the uses of global navigation satellite systems. It stands for precise point positioning. But what is that? Isn’t all GNSS positioning precise? Well, it’s a matter of degree.
Take GPS, for example. The most common kind of GPS signal use, that implemented in vehicle “satnav” units; mobile phones; and hiking, golfing and fitness receivers, is to employ the L1 C/A-code pseudorange (code) measurements along with the broadcast satellite orbit and clock information to produce a point position.
Officially, this is termed use of the GPS Standard Positioning Service (SPS). It is capable of meter-level positioning accuracy under the best conditions. There is a second official service based on L1 and L2 P-code measurements and broadcast data called the Precise Positioning Service (PPS).
In principle, because the P-code provides somewhat higher precision code measurements and the use of dual-frequency data removes virtually all of the ionospheric effect, PPS is capable of slightly more precise (and accurate) positioning. But because the P-code is encrypted, PPS is only available to so-called authorized users.
While meter-level positioning accuracy is sufficient for many, if not most applications, there are many uses of GNSS such as machine control, surveying and various scientific tasks, where accuracies better than 10 centimeters or even 1 centimeter are needed. Positioning accuracies at this level can’t be provided by pseudoranges alone and the use of carrier-phase measurements is required. Phase measurements are much more precise than code measurements although they are ambiguous and this ambiguity must be estimated and possibly resolved to the correct integer value.
Traditionally, phase measurements (typically dual-frequency) made by a potentially moving user receiver have been combined with those from a reference receiver at a well-known position to produce very precise (and accurate) positions. If done in real time (through use of a radio link of some kind), this technique is referred to as real-time kinematic or RTK.
A disadvantage of RTK positioning is that it requires reference station infrastructure including a radio link (such as mobile phone communications) for real-time results. Is there another way? Yes, and that’s PPP. PPP uses the more precise phase measurements (along with code measurements initially) on at least two carrier frequencies (typically) from the user’s receiver along with precise satellite orbit and clock data derived, by a supplier, from a global network. Precision, in this case, means a horizontal position accuracy of 10 centimeters or better.
In this month’s column, we travel along the road of PPP development, examine its current status, and look at where it might go in the near future.
In a 2009 GPS World “Innovation” article co-authored by Sunil Bisnath, the performance and technical limitations at the time of the precise point positioning (PPP) GPS measurement processing technique were described and a set of questions asked about the potential of PPP, especially with regard to the real-time kinematic (RTK) measurement processing technique.
Since the 2009 article, we’ve seen a significant amount of research and development (R&D) activity in this area. Many scientific papers discuss PPP and making use of PPP — a search on Google Scholar for “GNSS PPP” delivers nearly 7,000 results, and for “GPS PPP” more than 15,000 results! Will PPP eventually overtake RTK as the de facto standard for precise (that is, few centimeter-level) positioning? Or, in light of PPP R&D developments, should we be asking different questions, such as will multiple precise GNSS positioning techniques compete or complement each other or perhaps result in a hybrid approach?
In almost a decade, have we seen much in the way of positioning performance improvement, where “performance” can refer to positioning precision, accuracy, availability and integrity? Or, to some users, has the Achilles’ heel of PPP — the initial position solution convergence period — only been reduced from, for example, 20 minutes to 19 minutes? From such a perspective, all of this PPP research might not appear to have produced much tangible benefit. Advances have been made from this research and we will explore them here. Also, aside from many researchers working diligently on their own PPP software, there are now a number of well-established PPP-based commercial services — a number that has grown and been affected by the wave of GNSS industry consolidation over the decade. Consequently, there is much more to this story.
This month’s article summarizes the current status of PPP performance and R&D, and discusses the potential future of the technique. In the first part of the article, we will present brief explanations of conventional dual-frequency PPP, recent research and implementations, and application of the evolved technique to low-cost hardware. We will conclude the article with a rather dangerous attempt at near-term extrapolation of potential upcoming developments and conceivable implications.
Conventional PPP
The concept of PPP is based on standard, single-receiver, single-frequency point positioning using pseudorange (code) measurements, but with the meter-level satellite broadcast orbit and clock information replaced with centimeter-level precise orbit and clock information, along with additional error modeling and (typically) dual-frequency code and phase measurement filtering. Back in 1995, researchers at Natural Resources Canada were able to reduce GPS horizontal positioning error from tens of meters to the few-meter level with code measurements and precise orbits and clocks in the presence of Selective Availability (SA). Subsequently, the Jet Propulsion Laboratory introduced PPP as a method to greatly reduce GPS measurement processing time for large static networks. When SA was turned off in May 2000 and GPS satellite clock estimates could then be more readily interpolated, the PPP technique became scientifically and commercially popular for certain precise applications.
Unlike static relative positioning and RTK, conventional PPP does not make use of double-differencing, which is the mathematical differencing of simultaneous code and phase measurements from reference and remote receivers to greatly reduce or eliminate many error sources. Rather, PPP applies precise satellite orbit and clock corrections estimated from a sparse global network of satellite tracking stations in a state-space version of a Hatch filter (in which the noisy, but unambiguous, code measurements are filtered with the precise, but ambiguous, phase measurements). This filtering is illustrated in FIGURE 1, where measurements are continually added in time in the range domain, and errors are modeled and filtered in the position domain, resulting in reduced position error in time.
FIGURE 1. Illustration of conventional PPP measurement and error modeling in state-space Hatch filter, resulting in reduced position error in time.
The result is the characteristic PPP initial convergence period seen in FIGURE 2, where the position solution is initialized as a sub-meter, dual-frequency code point positioning solution, quickly converging to the decimeter-level in something like 5 to 20 minutes, and a few centimeters after ~20 minutes when geodetic-grade equipment is used (at station ALGO, Algonquin Park, Canada, on Jan. 2, 2017). For static geodetic data, daily solutions are typically at the few millimeter-level of accuracy in each Cartesian component.
FIGURE 2. Conventional geodetic GPS PPP positioning performance characteristics of initial convergence period and steady state for station ALGO, Algonquin Park, Canada, on Jan. 2, 2017.
The primary benefit of conventional PPP is that with the use of state-space corrections from a sparse global network, there is the appearance of precise positioning from only a single geodetic receiver.
Therefore, baseline or network RTK limitations are removed in geographically challenging areas, such as offshore, far from population centers, in the air, in low Earth orbit, and so on, and without the need for the requisite terrestrial hardware and software infrastructure. PPP is now the de facto standard for precise positioning in remote areas or regions of low economic density, which limit or prevent the use of relative GNSS, RTK or network RTK, but allow for continuous satellite tracking. These benefits translate into the main commercial applications of offshore positioning, precision agriculture, geodetic surveys and airborne mapping, which also are not operationally bothered by initial convergence periods of tens of minutes.
For urban and suburban applications, RTK and especially network RTK allow for near-instantaneous, few-centimeter-level positioning with the use of reference stations and regional satellite (orbit and clock) and atmospheric corrections. The use of double-differencing and these local or regional corrections allows sufficient measurement error mitigation to resolve double-differenced phase ambiguities. All of this additional information is not available to conventional PPP, limiting its precise positioning performance, but which is considered in PPP enhancements.
Progress on PPP Convergence Limitations
Over the past decade or so, PPP R&D activity can be categorized as follows:
Integration of measurements from multiple GNSS constellations, transitioning from GPS PPP to GNSS PPP;
Resolution of carrier-phase ambiguities in PPP user algorithms — in an effort to increase positional accuracy and solution stability, but foremost in an effort to reduce the initial convergence period; and
Use of a priori information to reduce the initial convergence and re-convergence periods and improve solution stability, making use of available GNSS error modeling approaches.
Unlike relative positioning, which makes use of measurements from the user receiver as well as the reference receiver, PPP only relies on measurements from the user site. This situation results in weaker initial geometric strength, and so the addition of more unique measurements is welcome. To make use of measurements from all four GNSS constellations (GPS, GLONASS, Galileo and BeiDou), user-processing engines must account for differences in spatial and temporal reference systems between constellations and numerous equipment delays between frequencies and modulations. The former can be done so that any number of measurements from any number of constellations can be processed to produce one unique PPP position solution. The latter requires a great deal of calibration, especially for heterogeneous tracking networks and user equipment (antenna, receiver and receiver firmware), most notably for the current frequency division multiple access GLONASS constellation.
FIGURE 3 shows typical multi-GNSS float (non-ambiguity-fixed) horizontal positioning performance at multi-GNSS station GMSD in Nakatane, Japan, on March 24, 2017. As with all modes of GNSS data processing, more significant improvement with additional constellations can be seen in sky-obstructed situations.
FIGURE 3. Typical conventional multi-GNSS PPP float horizontal positioning accuracy for station GMSD, Nakatane, Japan, March 24, 2017 (G: GPS, R: GLONASS, E: Galileo and C: BeiDou).
Related to multi-constellation processing is triple-frequency processing afforded by the latest generation of GPS satellites and the Galileo and BeiDou constellations. More frequencies mean more measurements, although with the same satellite-to-receiver measurement geometry as dual-frequency measurements. Again, additional signals require additional equipment delay modeling, in this case especially for the processing of GPS L1, L2 and L5 observables.
For processing of four-constellation data available from 20 global stations in early 2016, FIGURE 4 shows the average reduction of float (non-ambiguity-fixed) horizontal error from dual- to triple-frequency processing of approximately 40% after the first five minutes of measurement processing. In terms of positioning, this result, for this time period with a limited number of triple-frequency measurements, means a reduction in average horizontal positioning error from 43 to 26 centimeters within the first five minutes of data collection.
FIGURE 4. Average dual- and triple-frequency static, float PPP horizontal solution accuracy for 20 global stations. Data collected from tracked GPS, GLONASS, Galileo and BeiDou satellites in early 2016.
PPP with ambiguity resolution, or PPP-AR, was seen as a potential solution to the PPP initial solution convergence “problem” analogous to AR in RTK. Various researchers put forward methods, in the form of expanded measurement models, to isolate pseudorange and carrier-phase equipment delays to estimate carrier-phase ambiguities. These methods remove receiver equipment delays through implicit or explicit between-satellite single-differencing and estimate satellite equipment delays in the network product solution either as fractional cycle phase biases or altered clock products.
FIGURE 5 illustrates the difference between a typical GPS float and fixed solution (for station CEDU, Ceduna, Australia, on June 28, 2017). Initial solution convergence time is reduced, and stable few-centimeter-level solutions are reached sooner. For lower quality data, ambiguity fixing does not provide such quick initial solution convergence. Fixing is dependent on the quality of the float solution; and, for PPP, the latter requires time to reach acceptable levels of accuracy. Therefore, depending on the application, PPP-AR may or may not be helpful.
FIGURE 5. Typical float (red) and fixed (pink) GPS PPP horizontal solution error at geodetic station CEDU, Ceduna, Australia, on June 28, 2017.
To consistently reduce the initial solution convergence period, PPP processing requires additional information, as is the case for network RTK, in which interpolated satellite orbit, ionospheric and tropospheric corrections are needed since double-differenced RTK baselines over 10 to 15 kilometers in length contain residual atmospheric errors too large to effectively and safely resolve phase integer ambiguities. For PPP, uncombining the ionospheric-free code and phase measurements from the conventional model is required, to directly estimate slant ionosphere propagation terms in the filter state.
In this form, the model can allow for very quick re-initialization of short data gaps by using the pre-gap slant ionospheric (and zenith tropospheric) estimates as down-weighted a priori estimates post-gap — making these estimates bridging parameters in the estimation filter. Expanding this approach, external atmospheric models can be used to aid with initial solution convergence.
FIGURE 6 illustrates, for a large dataset, that applying a spatially and temporally coarse global ionospheric map (GIM) to triple-frequency, four-constellation float processing can reduce one-sigma convergence time to 10 centimeters horizontal positioning error from 16 to 6 minutes. If local ionospheric (and tropospheric) corrections are available and AR is applied, PPP (sometimes now referred to as PPP-RTK) can produce RTK-like results with a few minutes of initial convergence to few-centimeter-level horizontal solutions.
FIGURE 6. Averaged horizontal error from 70 global sites in mid-2016 using four-constellation, triple-frequency processing.
PPP Processing with Low-Cost Hardware
As the impetus for low-cost, precise positioning and navigation for autonomous and semi-autonomous platforms (such as land vehicles and drones) continues to grow, there is interest in processing such low-cost data with PPP algorithms. For example, it has been shown that with access to single-frequency code and phase measurements from a smartphone, short-baseline RTK positioning is possible. It has also been shown that similar smartphone data can be processed with the PPP approach. From the origins of PPP, it may be argued that single-frequency processing and many-decimeter-level positioning performance is not “precise.” But we will avoid such semantic arguments here (but see “Insights”), and focus on the use of high-performance measurement processing algorithms to new low-cost hardware. We are currently witnessing great changes in the GNSS chip market: single-frequency chips for tens-of-dollars or less; and boards with multi-frequency chips for hundreds-of-dollars. And these chips will continue to undergo downward price pressure with increases in capability, and be further enabled for raw measurement use in a wider range of applicable technology solutions. There are now a number of low-cost, dual-frequency, multi-constellation products on the market, with additional such products as well as smartphone chips coming soon.
To process data from such products with a PPP engine, modifications are required to optimally account for single-frequency measurements in the estimation filter, optimize the measurement quality control functions for the much noisier code and phase measurements compared to data from geodetic receivers, and optimize the stochastic modeling for the much noisier code and phase measurements. The single-frequency measurement model can be modified to either make use of the Group and Phase Ionospheric Calibration linear combination (commonly referred to as GRAPHIC) or ingest data from an ionospheric model. Due to the use of low-cost antennas, as well as the low-cost chip signal processing hardware, code and phase measurements suffer from significant multipath and noise at lower signal strengths; therefore, outlier detection functions must be modified. Also, the relative weighting of code and phase measurements must be customized for more realistic low-cost data processing.
FIGURE 7 compares the carrier-to-noise-density ratio (C/N0) values from ~1.5 hours of static GPS L1 signals collected from a geodetic receiver with a geodetic antenna, a low-cost receiver chip with a patch antenna, and a tablet chip and internal antenna, as a function of elevation angle. Received signal C/N0 values can be used as a proxy for signal precision. The three datasets were collected at the same time in mid-September 2017 in Toronto, Canada, with the receivers and antennas within a few meters of each other. The shading represents the raw estimates output from each receiver, while the solid lines are moving-average filtered results.
FIGURE 7. Carrier-to-noise-density ratios of ~1.5 hour of static GPS L1 signals from a geodetic receiver with a geodetic antenna, a low-cost receiver chip with a patch antenna, and a tablet chip and internal antenna, as a function of elevation angle.
Keeping in mind the log nature of C/N0, the high measurement quality of the geodetic antenna and receiver are clear. The low-cost chip and patch antenna signal strength structure is similar, but, on average, 3.5 dB-Hz lower. And the tablet received signal strength is lower still, on average a further 4.0 dB-Hz lower, with greater degradation at higher signal elevation angles and much greater signal strength variation.
The PPP horizontal position uncertainty for these datasets is shown in FIGURE 8. Note that reference coordinates have been estimated from the datasets themselves, so potential biases, in especially the low-cost and tablet results, can make these results optimistic. Given that only single-frequency GPS code and phase measurements are being processed, initial convergence periods are short and horizontal position error reaches steady state in the decimeter range. The geodetic and the low-cost results are comparable at the 2-decimeter level, whereas the tablet results are worse, at the approximately 4-decimeter level. Initial convergence of the geodetic solution is superior to the others, driven by the higher quality of its code measurements. The grade of antenna plays a large role in the quality of these measurements, for which there are physical limitations in design and fabrication. While geodetic antennas can be used, this is not always feasible, given the mass limitations of certain platforms or the cost limitations for certain applications.
FIGURE 8. Horizontal positioning error (compared to final epoch solutions) for geodetic, low-cost and tablet data processed with PPP software customized for single-frequency and less precise measurements.
Comments Regarding the Near Future
The PPP GNSS measurement processing approach was originally designed to greatly reduce computation burden in large geodetic networks of receivers by removing the need for network baseline processing. The technique found favor for applications in remote areas or regions with little terrestrial infrastructure, including the absence of GNSS reference stations. Given PPP’s characteristic use of a single receiver for precise positioning, various additional augmentations have been made to remove or reduce solution initialization and re-initialization interval to near RTK-like levels. But, to what end?
This question can be approached from multiple perspectives. From the theoretical standpoint, there is the impetus to maximize performance — millimeter-level static positioning over many hours, and few-centimeter-level kinematic positioning in a few minutes — by augmenting PPP in any way necessary. There is the academic exercise of maximizing performance without the need for local or regional reference stations – apparent single-receiver positioning, or truly wide-area augmentation. In terms of engineering problems, we can work to do more with less, that is, decimeter-level positioning with ultra-low-cost hardware, or the same with less, that is, few-centimeter-level positioning with low-cost hardware. And from the practical or commercial aspect, the great interest is for the implementation of evolved PPP methods for applications that can efficiently and effectively make use of the technology.
In terms of service providers, be it regional or global, commercial or public, there is momentum to provide enhanced correction products that are blurring the lines across the service spectrum from constellation-owner tracking to regional, terrestrial augmentation. A public GNSS constellation-owner, through its constellation tracking network, can provide PPP-like corrections and services. A global commercial provider with or without regional augmentation can provide similar services. The key is providing multi-GNSS state-space corrections for satellite orbits, satellite clocks, satellite equipment delays (fractional phase biases), zenith ionospheric delay and zenith tropospheric delay at the temporal and spatial resolution necessary for the desired positioning performance at reasonable cost, that is, subscription fees that particular markets can bear.
Given these correction products, PPP users have a greater ability to access a wide array of positioning performance levels for various new applications, be it few-decimeter-level positioning on mobile devices to few-centimeter-level positioning for autonomous or semi-autonomous land, sea and air vehicles. PPP can be used for integrity monitoring and perhaps safety-of-life applications where low-cost is a necessity and relatively precise positioning for availability and integrity purposes is required. For safety critical and high-precision applications, such as vehicle automation, PPP can be used alongside, or in combination with, RTK for robustness and independence with low-cost hardware. Such a parallel and collaborative approach would require a hybrid user processing engine and robust state-space corrections from a variety of local, regional and global sources, as we are seeing from some current geodetic hardware-based commercial services.
Near-future trends should also include more low-cost, multi-sensor integration with PPP augmentation. Optimized navigation algorithms and efficient user processing engines will be a priority as the capabilities of low-cost equipment continue to increase and low-cost integrated sensor solutions are required for mass-market applications. Analogous to meter-level point position GNSS, lower hardware costs should drive markets to volume sales, PPP-like correction services, and GNSS-based multi-sensor integration into more navigation technology solutions for various industry and consumer applications.
Clearly, the future of PPP continues to be bright.
SUNIL BISNATH is an associate professor in the Department of Earth and Space Science and Engineering at York University, Toronto, Canada. For over twenty years, he has been actively researching GNSS processing algorithms for a wide variety of positioning and navigation applications.
JOHN AGGREY is a Ph.D. candidate in the Department of Earth and Space Science and Engineering at York University. He completed his B.Sc. in geomatics at Kwame Nkrumah University of Science and Technology, Ghana, and his M.Sc. at York University. His research currently focuses on the design, development and testing of GNSS PPP software, including functional, stochastic and error mitigation models.
GARRETT SEEPERSAD is a navigation software design engineer for high-precision GNSS at u-blox AG and concurrently is completing his Ph.D. in the Department of Earth and Space Science and Engineering at York University. His Ph.D. research focuses on GNSS PPP and ambiguity resolution. He completed his B.Sc. in geomatics at the University of the West Indies in Trinidad and Tobago. He holds an M.Sc. degree in the same field from York University.
MANINDER GILL is a geomatics designer at NovAtel Inc. and concurrently is completing his M.Sc. in the Department of Earth and Space Science and Engineering at York University. His M.Sc. research focuses on GNSS PPP and improving positioning accuracy for low-cost GNSS receivers. He holds a B.Eng. degree in geomatics engineering from York University.
FURTHER READING
• Comprehensive Discussion of Technical Aspects of Precise Point Positioning
“Precise Point Positioning” by J. Kouba, F. Lahaye and P. Tétreault, Chapter 25 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
• Earlier Precise Point Positioning Review Article
“Precise Point Positioning Using IGS Orbit and Clock Products” by J. Kouba and P. Héroux in GPS Solutions, Vol. 5, No. 2, October 2001, pp. 12–28, doi: 10.1007/PL00012883.
“Precise Point Positioning for the Efficient and Robust Analysis of GPS Data from Large Networks” by J.F. Zumberge, M.B. Heflin, D.C. Jefferson, M.M. Watkins and E.H. Webb in Journal of Geophysical Research, Vol. 102, No. B3, pp. 5005–5017, 1997, doi: 10.1029/96JB03860.
“Reduction of PPP Convergence Period Through Pseudorange Multipath and Noise Mitigation” by G. Seepersad and S. Bisnath in GPS Solutions, Vol. 19, No. 3, March 2015, pp. 369–379, doi: 10.1007/s10291-014-0395-3.
“Global and Regional Ionospheric Corrections for Faster PPP Convergence” by S. Banville, P. Collins, W. Zhang and R.B. Langley in Navigation, Vol. 61, No. 2, Summer 2014, pp. 115–124, doi: 10.1002/navi.57.
“A New Method to Accelerate PPP Convergence Time by Using a Global Zenith Troposphere Delay Estimate Model” by Y. Yao, C. Yu and Y. Hu in The Journal of Navigation, Vol. 67, No. 5, September 2014, pp. 899–910, doi: 10.1017/S0373463314000265.
“External Ionospheric Constraints for Improved PPP-AR Initialisation and a Generalised Local Augmentation Concept” by P. Collins, F. Lahaye and S. Bisnath in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 17–21, 2012, pp. 3055–3065.
“Integer Ambiguity Resolution on Undifferenced GPS Phase Measurements and Its Application to PPP and Satellite Precise Orbit Determination” by D. Laurichesse and F. Mercier, J.-P. Berthias, P. Broca and L. Cerri in Navigation, Vol. 56, No. 2, Summer 2009, pp. 135–149.
“Resolution of GPS Carrier-phase Ambiguities in Precise Point Positioning (PPP) with Daily Observations” by M. Ge, G. Gendt, M. Rothacher, C. Shi and J. Liu in Journal of Geodesy, Vol. 82, No. 7, July 2008, pp. 389–399, doi: 10.1007/s00190-007. Erratum: doi: 10.1007/s00190-007-0208-3.
“Isolating and Estimating Undifferenced GPS Integer Ambiguities” by P. Collins in Proceedings of ION NTM 2008, the 2008 National Technical Meeting of The Institute of Navigation, San Diego, California, Jan. 28–30, 2008, pp. 720–732.
• Precise Positioning Using Smartphones
“Positioning with Android: GNSS Observables” by S. Riley, H. Landau, V. Gomez, N. Mishukova, W. Lentz and A. Clare in GPS World, Vol. 29, No. 1, January 2018, pp. 18 and 27–34.
For Black History month in February, the Free-Lance Star of Fredericksburg, Virginia, profiled a woman few of us know about — Gladys West.
Capt. Godfrey Weekes, then-commanding officer at the Naval Surface Warfare Center Dahlgren Division, described to the newspaper the “integral role” played by West.
Gladys West’s work helped develop the Global Positioning System. (Photo: U.S. Navy)
“She rose through the ranks, worked on the satellite geodesy and contributed to the accuracy of GPS and the measurement of satellite data,” he said. “As Gladys West started her career as a mathematician at Dahlgren in 1956, she likely had no idea that her work would impact the world for decades to come.”
West collected data from the satellites, focusing on information that helped to determine their exact location as they transmitted from around the world. Data was entered into large-scale super computers that filled entire rooms, and she worked on computer software that processed geoid heights (precise surface elevations).
As a girl growing up in Dinwiddie County, Virginia, Gladys knew she didn’t want to work in the fields or a tobacco factory like her parents did.
“I was ecstatic,” she said of her career. “I was able to come from Dinwiddie County and be able to work with some of the greatest scientists working on these projects.”
Jim Colvard, technical director at NSWC Dahlgren from 1973 to 1980, knew West as a student in his graduate program and as a professional employee. “She was an excellent student and a respected and productive professional,” he wrote in an email. “Her competence, not her color, defined her.”
Gladys West, at Dahlgren with Sam Smith in 1985, looks over data from the Global Positioning System she helped develop. (Photo: U.S. Navy)
New users can upload their first UAV, satellite or aerial image project to obtain digital surface model (DSM), digital terrain model (DTM), point cloud and orthomosaic outputs.
Along with optimal results, users also receive tailored feedback, recommendations and training from SimActive experts.
The service requires no obligation, and is based on Correlator3D software, building on more than a decade of innovation on computer vision algorithms, a subfield of artificial intelligence. Quick turnaround is also possible due to the speed of the software and extensive use of GPU.
“Our new offer is unique to get the best possible results from the very first project onward,” said Philippe Simard, president of SimActive. “Following this, users are trained with industry-leading technology, custom advice and necessary knowledge for successful mapping.”
Tersus GNSS is now offering its David real-time kinematic (RTK) GNSS receiver with seven new base/rover kits.
Tersus GNSS is a provider of centimeter-accuracy GNSS RTK solutions. The Tersus David GNSS receiver with its components create an affordable solution delivering high-precision signal reception, integrated in a small, and lightweight package.
The David GNSS receiver supports GPS L1/L2, GLONASS G1/G2 and BeiDou B1/B2. With David, surveyors users can take full advantages of common platforms such as smartphones, tablets or traditional handheld modules to collect data.
Coupled with an external antenna, the Survey App and post-processing software, the David GNSS receiver is a low-cost solution for all survey applications, including real-time RTK positioning and data collection for PPK.
Four (4) GB on board an embedded multimedia card (eMMC) makes it easy to save data for post processing. The compact, IP67-rated enclosure and versatile accessories alleviate most inconveniences encountered in field work.
“The launch of David GNSS Receiver marks a major step forward for Tersus as well as for surveying professionals,” said Xiaohua Wen, founder and CEO of Tersus. “The David is a cost-efficient and palm-sized GNSS receiver. Tersus is constantly working to make each surveying task easier and more productive by providing high-quality GNSS RTK surveying equipment. Our focus is on enabling surveying professionals make data collection more convenient, post (data collection) processing more accurate, and better equipping them to do surveying in the field.”
By Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura
All images provided by the authors
A real-time system combining a simulator and a GNSS propagation model reproduces an authentic multipath environment. The propagation model relies on a 3D-model reconstruction of the urban environment, which generates a multipath signature strictly dependent on the location of the receiver’s antenna. This yields important results for a moving vehicle, which may be affected by very different multipath conditions depending on trajectory and location.
Positioning and navigation can be degraded in urban environments by multipath, and the error can increase considerably if not properly compensated. In situations where the line-of-sight (LOS) is obscured by surrounded buildings, the receiver may still be able to navigate by using the non-line-of-sight (NLOS) signal, which originates from single or multiple reflections/diffractions of the GNSS signal.
The use of 3D models has been one of the preferred solutions to recreate the multipath environment as seen by a GNSS device. This solution brings the capability to generate a multipath signature that is representative of the position of the antenna in a specific time and space. However, this solution comes with a certain degree of complexity. In fact, an accurate 3D model is required to simulate the obscuration of the GNSS signal, and a good propagation model is needed to generate phenomena like reflection and diffraction.
Figure 1. Example of propagated signal simulation. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura))\
3D models have become more accurate and widely available and are mainly used to predict the satellite availability in specific locations, for example in evaluating the signal availability in urban canyon, and for both reflection and diffraction. Other uses of 3D models are as an aiding tool to assist navigation, sometimes together with an INS solution.
In this article, we present a novel real-time system capable of simulating realistic multipath in different environments. The system can simulate multiple GNSS constellations and is comprised of a GNSS simulator interfaced to a propagation model. The system can create a whole range of signals, effects, error models and trajectories in a real-time closed loop. The propagation model controls the simulation of multipath from the interaction of the GNSS signal with the 3D scene and objects. This article describes a novel real-time system for the simulation of realistic multipath in different environments and compares simulated and field-test data. The comparison is based on signal availability, horizontal error, carrier-to-noise (C/N0), pseudorange and Doppler residuals.
RAY-TRACING WITH 3D MODELING
The model simulates the propagation of GNSS signals in constrained environments, considering obscurations and multipath. It uses a proprietary ray-tracing kernel (based on bounding volume hierarchy techniques using processing unit [GPU] resources) coupled with geometrical optics and uniform theory of diffraction to compute the interaction between the signal and the local environment. The computation uses as main input a synthetic environment (that is, geometrical and physical modeling of a real or realistic environment) to assess the impact of obscurations related to signal availability issues and multipath (the cause of fading effects and performance problems).
The objective of ray-tracing is to find all the possible paths from the observer to the source of the signal considering a limited number of interactions per emitted rays. A ray-tracer (or ray-tracing algorithm) uses a primary grid to cast primary rays. Then, it iteratively computes the possible interactions between these rays and the virtual scene (often defined using triangles). If those interactions exist (if they comply with the law of physics) and if the number of interactions to reach the emitter is below the maximum number of interactions set by the user, then a ray (or multipath) is created. This is a deterministic method that can be used to calculate the obscuration due to the local environment (and therefore detect the signal availability) and the geometrical characteristic of the computed path. Combined with physics modeling, path attributes such as received power, delay, Doppler, and phase are also provided.
The main characteristics of ray-tracing techniques to model GNSS propagation are:
All the signals arriving at the receiver can be model-based on the virtual environment.
As it is a deterministic method, the more realistic the environment modeling, the more compliant with reality the results. Moreover, the simulation results are repeatable.
The specular multipath can be displayed in 3D, and the attributes (for example, receiver power, phase, polarization, Doppler, geometry of the ray) are known. For example, this is relevant when the effect and signature of the environment on the propagation signal need to be studied and understood.
Nonetheless, ray-tracing techniques must account for three major difficulties:
They are time-consuming algorithms. Indeed, depending on the complexity of the scene (defined in terms of the number of triangles), a combinatorial problem to find the possible multipaths reaching the receiver makes the ray-tracer very resource-demanding. That is the reason why the most difficult task to achieve during the coding of a real-time ray-tracing algorithm is to develop acceleration techniques to quicken the computation process. Several solutions exist to either improve the intersection determination (for instance, based on spatial hierarchies such as bounding volume hierarchy [BVH] techniques), or to decrease the number of cast rays (often based on adaptive sampling techniques), or even to replace rays with beams or cones. Moreover, it is possible today to use the resources of graphic boards to accelerate the computation. Indeed, as ray-tracing can be coded by a large number of primary functions that can be treated simultaneously, it can be easily ported into GPU.
Their accuracy depends on the resolution of the primary grid. Details and therefore rays may be missed if the 3D scene is made of small details. This issue is called aliasing. Aliasing artefacts are raised for instance in parts of the scene with abrupt changes (such as edges) or in complex areas with lots of constituent objects. Solutions (or antialiasing techniques) exist to overcome this issue such as adaptive or stochastic samplings.
When it is combined with geometrical optics, these algorithms only compute the specular rays. Even if some techniques exist to model the scattering signals, only physical optics can render the global signal with high fidelity.
MULTIPATH SIMULATION SYSTEM
The proposed system can model two of the main propagation issues encountered in urban environments, such as obscuration (which leads to limitations in signal availability) and multipath (which generates interference that causes fading of the signal and positioning errors). To model realistically such a complex phenomenon, the system uses a GPU ray-tracing algorithm combined with geometrical optics and uniform theory of diffractions. The ray-tracing algorithm relies on 3D-model reconstructions of the urban environment. The computed obscuration and multipath effects are then used to generate signal corrections (in terms of power, delay and Doppler variation) to be used in the GNSS simulator, which generates the carrier, code and navigation messages for different GNSS constellations into a single RF output. Some of the advantages of this system is its ability to run in real time, and to visually show all the reflections/diffractions of the GNSS signals that cause multipath interference.
Figure 2 shows the diagram of the system set up in conductive mode. The system includes a SE-NAV PC controller, simulator software suite controller, GNSS simulator and device under test (DUT). A different mode is also available called over the air (OTA). This mode uses an anechoic chamber and a set of antennas distributed uniformly to generate the RF signal including the multipath. The DUT can then be placed at the center of the chamber and will be able to receive LOS and NLOS signals from different angles of arrival.
Figure 2. System diagram that shows propagation simulator controller (top), the GNSS simulator (bottom) and the device under test connected to the RF output of the simulator. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
The GNSS simulator software suite is used to generate and control the generation of the satellite signals (including multipath) at RF, whilst the propagation simulator is used to calculate the propagation information (delay, Doppler and attenuation) of the reflected signals through a 3D urban model. The propagation software is interfaced with GNSS simulator software by means of a package of remote-control facilities that greatly enhances the flexibility of the propagation simulator. Those commands can be sent and received through the transmission control protocol/use datagram protocol (TCP/UDP) with different data streaming rates (10 Hz was used for this article).
It is also important to point out that the propagation simulator computes all the possible multipath signal generated by the 3D model given the position of the satellites and receiver. However, the physical limitation of the number of channels in the simulator causes the rejection of some rays. This rejection or filtering process can be done according to power (used in this article) or delay.
EXPERIMENT SET-UP
A set of different field-test campaigns where carried out in August 2016. Each campaign aimed to evaluate the ability of the system to assess the performances of a GNSS receiver using simulated signals in urban environments. Figure 3 shows the trajectory (blue line) used for the experiment in an urban environment — San Jose, California — with a static (a) and dynamic (b) scenario.
Figure 3. A set of three measurement campaigns where carried out during Aug. 9–10, 2016: a) urban environment with static antenna; b) urban environment with dynamic antenna. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
Figure 4 shows the 3D scene used to replicate the San Jose urban environment. The buildings in close proximity of the antenna (green area in Figure 4b) contain details like material, 3D facade and windows. In contrast, the buildings far from the antenna were only corrected for height, and the material was modeled as concrete only.
Figure 4. The San Jose model contained most of the details around the receiver antenna (b), with only height corrected for buildings far from the antenna (c). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
An exception was made for one building in San Jose because its complex architecture was believed to contribute to more reflected rays than would a more simplistic box (concrete) model (Figure 5).
Figure 5. Improvement (right) in one San Jose building because its complex architecture was believed to generate more reflections than the more simplistic box model (left). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
EXPERIMENT RESULTS
A direct comparison of C/N0 power, pseudorange residual, and Doppler residual was performed between the field test and simulation.
San Jose Static Results. Figure 6 shows the results obtained from the San Jose static scenario for satellites PRN02 and PRN06: C/N0 ratio, pseudorange residual and Doppler residual for field test (blue line) and simulation (red line). Although the simulation sometimes creates deeper fading than in the field test, a first comparison indicates a good correlation of simulated data with field-test data.
Figure 6. Carrier-to-noise ratio (top), pseudorange residual (middle) and Doppler residual (bottom) for PRN 02 (left column) and PRN 06 (right column). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
The signature of the multipath caused by this urban environment is visibly captured in the simulation. More interestingly, the pseudorange residuals and, to a lesser extent, Doppler residuals also indicate that the model is replicating the dynamics of the multipath environment in close correlation with the field test.
Figure 7 shows the C/N0 obtained from the field data (blue), and simulated data (red) with only obscuration (a) and with obscuration and multipath (b) for the static scenario.
It can be noticed that the receiver can still track PRN02 without the LOS, therefore, relying on just the NLOS signal. This can be clearly seen in Figure 7a where a sudden drop in power is associated to an obscuration of the same satellite (based on our 3D urban model).
Figure 7b shows the C/N0 obtained from the simulation (red line) when both obscuration and multipath were enabled. In this case the receiver could track the satellite even in the case of only NLOS as in the field test.
Figure 7. Carrier-to-noise ratio for satellite PRN02 with only obscuration (a) and with multipath (b). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
The positioning error for the San Jose static scenario is shown in Figure 8a. The simulation and field-test data have a comparable error. The error is relatively big at the beginning of the simulation and decreases after time 20.6. At the time 22.3, a moderate increase in the positioning error is visible in the field data until the end of the test. The simulation also shows a similar trend in this last part of the test, but tends to generate a higher positioning error.
The satellite availability is shown in Figure 8b for both simulated (red) and field test (blue). The availability of the satellites generated with simulated data is in close relationship with the field data. However, some satellites could not be tracked in the simulation.
Figure 8. a) positioning error for field-test (blue) and simulation (red); b) satellite availability for field data (blue) and simulation (red). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
The importance of the accuracy of the 3D scene is evident in this example. In fact, we noticed that one of the buildings that was simulated as a simple concrete box was more complex in the real environment. Therefore, we applied some modifications to scene, as in Figure 9.
Figure 9. 3D scene improvement. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
After those changes, a general improvement in the results was visible, but most importantly, the missing satellites could finally be tracked by the receiver (Figure 10).
Figure 10. Satellite availability for field data (blue) and simulation after scene improvement. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
SAN JOSE DYNAMIC TEST RESULTS
Similar results were obtained with the dynamic test in San Jose. Figure 11 shows the results obtained for satellites PRN12 and PRN24. The walking trajectory included two points where the antenna was stopped because of a traffic light. Those points correspond to a relatively flat C/N0 that can be clearly seen in the field test and simulation data for both PRNs. When, instead, the antenna was moving, a higher variation in the C/N0 is noticeable in both simulation and field test.
Figure 11. Carrier-to-noise ratio (top), pseudorange residual (middle), and doppler residual (bottom) for PRN 12 (left column) and PRN 24 (right column). (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
Figure 12a illustrates the positioning error obtained from simulated (red) and field test (blue). The first part of the simulation produced an error smaller than the one obtained from field data. However, from the time 19.48, a good agreement can be seen. The satellite availability is also shown in Figure 12b. This last result was obtained with the improved model described in Figure 9.
Figure 12. (a) Positioning error for field-test (blue) and simulation (red); (b) satellite availability for field data (blue) and simulation (red) after scene improvement. (Image: Tommaso Panicciari, Mohamed Ali Soliman and Grégory Moura)
CONCLUSIONS AND FUTURE WORK
A new real-time system for multipath simulation is designed to generate realistic multipath that depends on time, position and type of urban environment. The 3D scene is used to calculate the multipath (reflection and diffraction) caused by the buildings and objects around the antenna.
Some first results demonstrated that realistic multipath can be generated by simulating reflections and diffractions even with a simple 3D model. However, the inclusion of finer details in the model can improve the simulation and make it even closer to reality.
As always, simulation interest is a tradeoff between reliability in all conditions and efforts to adapt (that is, to specify) a generic and simple model. The added value of our model consists in its simplicity and its good compliance with field data.
Ray-tracing techniques coupled with geometrical optics and uniform theory of diffraction are efficient and simple methods to simulate the propagation of GNSS signals in complex urban environments. Their efficacy is demonstrated by a good agreement between simulation and field measurements. Some discrepancies still exist and are due to the limitations of such a model:
The accuracy of the model is never perfect and, as ray-tracing is a deterministic method, the returned results strongly depend on the quality of the input data used to generate the model.
Geometrical optics is a simple (but efficient) method. Only specular rays are modeled, thus the system won’t be able to generate all the signals coming from other phenomena such as scattering. Another limitation is given by the hardware. In fact, the number of simulated multipath depends on the number of available channels in the simulator.
The simulation parameters try to mimic the field conditions. However, the simulated trajectory is approximated, and other factors like pedestrian motion, vegetation (isolated trees or forest) and traffic may contribute to reduce some of the discrepancies that can be observed between simulation and field
All of these limitations can explain the differences between simulated and measured data. Currently, the impact of vegetation (forest and/or isolated trees) models, pedestrian motion and traffic on the multipath signal can also be simulated and their performances are under evaluation.
ACKNOWLEDGMENTS
We thank Colin Ford and Ajay Vemuru from Spirent Communications and Antoine Boudet, Yann Dupuy, Arnold Duquesne and Paul Pitot from OKTAL Synthetic Environment.
MANUFACTURERS
The system described in this article consists of a Spirent GNSS simulator equipped with a SimGEN software suite and the SE-NAV simulator developed by OKTAL Synthetic Environment. SE-NAV is interfaced with SimGEN via the SimREMOTE protocol, a real-time control and motion API.
Tommaso Panicciari obtained a Ph.D. in telecommunications from the University of Bath (UK). He is a software/project engineer at Spirent Communications where his main activity focuses on spoofing and multipath simulation.
Mohamed Ali Soliman is completing a master’s degree in telecommunications with business at University College London. He is a product manager at Spirent Communications, managing multiple products including the multipath simulation offering.
Grégory Moura graduated from the French Institute of Aeronautics and Space with an M.S. in cosmology from Université de Toulouse. He manages the GNSS activities of the French company OKTAL Synthetic Environment.
Hemisphere GNSS has released the new single-frequency, multi-GNSS Vector V123 and V133 all-in-one smart antennas with integrated Atlas L-band designed for professional and commercial marine applications.
The company made the announcement at the Oceanology International conference being held this week in London, U.K.
Powered by Hemisphere’s Crescent Vector technology, the new V123 and V133 are multi-GNSS compass systems using GPS, GLONASS, BeiDou, Galileo and QZSS for simultaneous satellite tracking to offer heading, position, heave, pitch and roll. Both antennas support NMEA 0183 and NMEA 2000.
The V123 and V133 thrive in radar/ARPA, AIS, ECDIS, side-scan survey, multi- and single-beam surveys, dredging and general navigation applications.
The V123 and V133 rugged smart antennas combine Hemisphere’s recently announced Crescent Vector H220 OEM board and two superior multipath- and noise-rejecting antennas (spaced 50 centimeters apart) in a single enclosure.
The smart antennas require only a single power/data cable connection for fast and reliable installations, even in the presence of strong radio transmissions. Both Vector models provide 0.3-degree heading accuracy and sub-meter DGPS accuracy, as well as optional 0.5-meter Atlas L-band accuracy.
The V133 includes all the features of the V123 and adds the capability of receiving differentially corrected data from land-based beacon stations. Ease of installation and no maintenance or servicing enhances the simplicity of these new Vector models.
“The Vector V123 and V133 GNSS compasses represent significant enhancements to our industry leading models they replace, providing even greater performance, improved robustness and excellent value,” said Miles Ware, director of marketing at Hemisphere GNSS. “Users now have an even higher performing all-in-one Vector for their commercial and professional needs with the addition of BeiDou, Galileo, and QZSS as well as Atlas L-band corrections.”
Story maps combine geographic data withmultimediato tell a story and present information in a useful, interesting way.
While many story maps are designed for general, non-technical audiences, some story maps can also serve highly specialized audiences. They use the tools of GIS, and often present the results of spatial analysis, but don’t require their users to have any special knowledge or skills in GIS.
“Story maps use geography as a means of organizing and presenting information. They tell the story of a place, event, issue, trend or pattern in a geographic context,” explains Esri’s press staff in a blog. “They combine interactive maps with other rich content — text, photos, illustrations, video and audio — within intuitive user experiences.”
Haven’t yet dipped your toe into Story Maps? This Esri blog takes users through story map creation step by step.
Below are six visual narratives that provide timely information using Esri’s Story Map creation tools.
Faces Show Personal Impact of Opioid Epidemic
The National Safety Council is adopting the Celebrating Lost Loved Ones map, which allows family and friends of those lost to the opioid epidemic to place an image and description of their late loved one on an interactive map. The project helps raise awareness of the broad impact of the opioid crisis and advances the council’s mission of ending opioid deaths. Unintentional opioid overdose deaths totaled 37,814 in 2016.
Jeremiah Lindemann, a solution engineer for Esri, created the map in 2016 following the death of his younger brother. Since its launch, the map has gathered more than 1,300 memorials from people across the U.S.
The map has been a crowdsourced effort, allowing grieving friends and family members to honor their loved ones, share their stories with others and find a supportive community in return.
Communities Potentially Affected by DACA Policy Changes
When elected officials talk about changing our immigration system, just who and where are people affected? That’s the question Esri is trying to help answer with a new interactive story map that explores communities with the highest shares of non-citizen residents and DACA (Deferred Action for Childhood Arrivals) recipients.
The map shows estimates on DACA eligible, recipients, and annual GDP loss from removing DACA workers by congressional district. Data comes from USC’s Dornsife Center for Immigrant Integration.
The size of the symbol shows the estimate of DACA recipients, and the color of the symbol shows the estimated GDP loss from removing DACA workers. This map shows that the economies of many states in the Southwest and several major urban cities could be substantially disrupted if DACA recipients are no longer permitted to work.
The Ever-Changing Minimum Wage
National, state and local government policies toward the minimum wage vary widely and are continually changing. On Jan. 1, new or adjusted minimum wage policies took effect in 18 states and territories. Varying rates, policies, and impacts across the nation make it challenging to understand the minimum wage landscape.
This Esri story map provides an overview of the the nation’s changing minimum wage policies. A few notable findings:
At the highest level, the variability of minimum wage policies from state to state is striking — this ranges from some states in the South that don’t even require a minimum wage, to places like D.C. that have a $12.50 minimum wage (currently the highest for a state or territory).
Similarly, the number of cities and counties that have taken it upon themselves to raise wages locally is impressive; these cities and counties have robust plans for raising minimum wages over the next few years.
Regardless of an area’s minimum wage, all states fail to guarantee minimum wages that actually match up to the cost of living for their respective areas. As such, there is a growing divide between states that have raised minimum wages and are at least bringing minimum wages closer to the cost of living, versus those states that are slower to raise minimum wages (or don’t raise wages at all) and fall much further below the local cost of living.
Even while minimum wages have nominally increased, inflation has devalued the dollar in such a way that even in 2018 some wages today have less purchasing ability than nominally lesser wages in the 1970s.
Ireland Encourages Emmigrees to Come Home
Like much of Ireland, the history of County Donegal is inextricably wedded to the geography of migration. Now county officials are using a story map to try and woo émigrés back to the Emerald Isle.
The Irish government views the loss of its citizens so seriously that a minister for diaspora affairs was appointed to the Irish cabinet in 2014.
“Ireland’s Call — To Return Its Global Diaspora Home” displays key factors to assist those in contemplating returning. The story map launches the Global Skills Locator to link its global diaspora with job opportunities back home.
Smart City 3.0 Book Explained
Esri China (Hong Kong) Limited uses the story map tools in a unique way — to highlight its new book Smart City 3.0. The book and map discuss artificial intelligence, the internet of things, robotics and the sharing economy, and how all of them are shaping a new phase of development for the smart city.
Hurricane Harvey’s Lasting Effects
Within cities, poor communities often live in segregated neighborhoods with higher flood risks. This is especially true in Houston, where Hurricane Harvey hit this past August.
As in previous disasters like Katrina and Sandy, the heaviest cost of Harvey’s destruction is likely going to be borne by the most vulnerable communities in its path.
Humanitarian aid organization Direct Relief’s interactive Esri maps used the Centers for Disease Control and Prevention’s social vulnerability index to show the geographic distribution of households with elderly or disabled members (in orange), immigrant and limited English-speaking populations (in purple), and pockets of poverty (in green). The darker the color, the higher the concentration of these factors in each region.
Learn more about story maps and how to create them here.
The U.S. Air Force has awarded a GPS III satellite launch contract to SpaceX. This is the third GPS III launch contract awarded; the previous two also were awarded to SpaceX.
SpaceX will receive a $290,594,130 firm-fixed-price contract for launch services to deliver three GPS III missions (1 base and 2 options) to the intended orbit using two Evolved Expendable Launch Vehicles (EELVs).
A SpaceX Falcon 9 rocket lifts off from Space Launch Complex 4E at Vandenberg Air Force Base, California, Jan. 14, 2017. (Photo: SpaceX)
The launch contract provides the government with a total launch solution for the GPS III mission, including launch vehicle production, mission integration, launch operations and spaceflight certification. The launches will take place from Cape Canaveral Air Force Station or Kennedy Space Center, Florida.
The GPS III missions are planned to launch between late 2019 and 2020.
“The three GPS III missions will deliver sustained, reliable GPS capabilities to America’s warfighters, our allies and civil users,” the U.S. Air Force said in a statement. GPS provides positioning, navigation and timing service to civil and military users worldwide.
In a second launch services contract, United Launch Alliance has been awarded a $351,839,510 firm-fixed-price contract for launch services to deliver Air Force Space Command (AFSPC)-8 and AFSPC-12 satellites to the intended orbit.
This is the fourth competition under the current Phase 1A procurement strategy. Both launch service contract awards strike a balance between meeting operational needs and lowering launch costs through reintroducing competition for National Security Space missions, according to Los Angeles Air Force Base, which made the announcement.
“The competitive award of these two EELV launch service contracts directly supports Space and Missile Systems Center’s mission of delivering resilient and affordable space capabilities to our nation while maintaining assured access to space,” said Lt. Gen. John F. Thompson, U.S. Air Force Program Executive Officer for Space and SMC commander.
SpaceX won two previous GPS III launch contracts, one awarded in March 2017 and one in April 2016.
Wingtra has officially launched the WingtraOne PPK high-precision mapping drone. Wingtra said its drone, which features vertical take-off and landing, is designed to set a new benchmark for large-scale surveying and mapping applications.
WingtraOne PPK offers large area coverage, ultra-high accuracy and brilliant image resolution. It features an advanced PPK module and high-quality cameras like the 42-megapixel full-frame camera Sony RX1RII, it is now possible to reach down to 1-centimeter absolute accuracy in aerial mapping.
To prove this accuracy claim, the Wingtra team performed test flights in a gravel quarry. The process was documented and is now explained in a white paper on the company website.
Conventional drone mapping on centimeter accuracy requires ground control points (GCPs) to correct the final map. Besides requiring additional surveying equipment and being extremely time consuming, setting up GCPs might be downright risky or just not possible in the area of interest.
More advanced solutions achieve similar levels of accuracy by using GPS correction technology for the georeferencing of the aerial imagery: namely RTK (real-time kinematics) or PPK (post processed kinematics).
RTK requires real-time base station connectivity and corrects GPS signals during the flight, while PPK corrects them after the flight and therefore offers greater robustness and consistency.
Moreover, PPK is independent from base stations or base station networks. It is highly reliable, accurate and time saving to use, Wingtra said. Neither special flight preparations nor intensive post-processing steps are required to achieve down to 1-cm accurate aerial maps.
