Trimble has expanded its CenterPoint RTX Fast GNSS correction service coverage area in North America.
Additional states and provinces now covered by Trimble RTX Fast include Alabama, California, Florida, Georgia, Michigan, Mississippi, New Mexico, North Carolina, Ohio, Oregon, South Carolina and Washington, and Alberta and Ontario Canada.
Trimble RTX Fast reduces convergence time, allowing customers to achieve horizontal positioning accuracy of better than one inch (2 centimeters), in as fast as one minute.
Now, with CenterPoint RTX more farmers, surveyors, GIS professionals and construction contractors can experience the RTK-level accuracy of traditional cellular-based Virtual Reference Station (VRS) networks, while benefiting from the versatility of a satellite-delivered correction service, Trimble said.
“Trimble RTX technology has continually evolved since its launch in 2011 with improving accuracy and reduced convergence times,” said Patricia Boothe, vice president of Trimble’s Advanced Positioning Division. “This network expansion demonstrates our commitment to bringing the market-leading performance of Trimble RTX Fast to more users, in more geographies around the world.”
Trimble’s RTX network is currently available throughout most of the world, with the RTX-Fast network coverage available in select geographies in the U.S., Canada and throughout most of Europe, when using Trimble RTX compatible GNSS receivers. Subscriptions are available through Trimble’s Authorized Business Partners or Trimble’s online store.
With Orolia’s Skydel SDX API, GMV has developed a “Time Plugin” that allows to batch edit the numerical values associated to TGDs, UTCO and Leap Seconds. (Image: GMV)
GMV uses the flexibility of Skydel SDX to simulate GNSS timing events.
The upcoming GPS Week Number Rollover (WNRO) on April 6 is a known feature of GPS that occurs only every roughly 20 years and thus can be anticipated and tested in a receiver using a GNSS simulator.
A less-known rollover effect is also present in the Week Number (WN) associated to the UTC Offset (UTCO) transmitted by GPS. This WN has only 8 bits, which implies a range of values from 0-255, equivalent to a rollover every roughly 5 years. A possible failure in the receiver interpretation of the UTCO WN would not have such dramatic effects as a general GPS WNRO, but it could seriously affect the output of a GPS timing receiver.
Other than WN rollovers, un-programmed GNSS timing glitches are extremely rare but not impossible. A well known case is the GPS anomaly of Jan. 25-26, 2016, when around half of the satellites in the constellation transmitted an incorrect UTCO value during 12 hours. The transmitted value was of the order of 13 microseconds, when the correct value is usually of the order of just a few nanoseconds. This caused many timing receivers around the world to provide an incorrect timing information or to fail altogether.
Other fields in the GPS navigation message that could potentially be corrupt and cause incorrect timing output are Timing Group Delays (TGDs), and Leap Second information. In general, any event related to the contents of the GPS navigation message can be simulated by editing the associated bits in the message, for each satellite PRN.
However this is a cumbersome and prone-to-error task. Orolia’s Skydel SDX simulator provides an Application Programming Interface (API) that allows to develop SDX extensions to tackle particular simulation problems in an easy and flexible way.
By using this API, GMV has developed a “Time Plugin” that allows to edit directly on a user interface the numerical values associated to TGDs, UTCO, and Leap Seconds, per individual satellite PRN. These values are then converted to bits in the GPS message and transmitted in the RF stream to feed the receiver under test. Events like, for example, the GPS anomaly of Jan. 25-26, 2016, can be easily tested (as shown in the screenshot).
A European Union project has designed and prototyped the ESCAPE GNSS Engine (EGE), a positioning module intended to enable autonomous or semi-autonomous driving functions.
Automated vehicles are on the way, and the European GNSS Agency (GSA) sees satellite navigation as a core technology that will help to ensure their safe operation. At the recent Mobile World Congress in Barcelona, the GSA shared its space with the ESCAPE project, an EU-funded initiative that has developed a unique positioning module for autonomous or semi-autonomous driving.
Autonomous vehicles will feature both sensor-based and connection-based solutions for a variety of vehicle services. Ultimately, the GSA sees a “converged solution” as the best alternative, combining the strengths of both approaches. By integrating sensor data and connectivity-based information, operators can reduce the need for the most expensive sensors and at the same time save money on infrastructure.
The Fundamental Elements-funded ESCAPE project has designed and prototyped the ESCAPE GNSS Engine. It is a unique positioning module that combines precision GNSS and 4G connectivity, for the highly accurate and reliable positioning capabilities required to make automated driving a reality.
The ESCAPE GNSS Engine. (Photo: GSA)
“This is an onboard unit for autonomous vehicles,” said Jessica Garcia Soriano, R&D engineer of the Advanced Communications Business Unit at Ficosa. “It is equipped with a very good GNSS receiver made by STMicroelectronics. This was actually the first dual-frequency GNSS receiver made for the automotive market.”
Dual-frequency is of course a real differentiator for Galileo, as the world’s leading provider of dual-frequency GNSS signals. This means added precision and robustness and it helps enormously with multi-phase errors and other urban canyon issues in city-driving scenarios.
“We also have a very good positioning solution provided by GMV, another Spanish company. They are experts in these kinds of solutions. The outputs from this solution are very accurate. So we have GNSS of course, including Galileo, and apart from this you have a modem inside, a 4G modem that gets GNSS corrections from the internet, so this helps to provide better positioning. And apart from this you have inside the same module an inertial measurement unit [IMU]. This is a sensor, a device that senses acceleration and has a gyroscope, so this information also helps in providing good positioning.”
The ESCAPE unit also provides for the integration of other data from the vehicle. “That means vehicle odometry, for instance, you can have camera information, or information from maps that are stored in the vehicle, among others” Garcia said.
The market is ready
“One of our important goals is to provide a low-cost system,” Garcia said. “There are other very good positioning systems that are being developed that can be based on some very advanced technologies, such as LiDAR for instance, but this is very expensive. So our target is to develop and build a prototype of a system that could be installed in all vehicles, for the whole market. And so we are combining GNSS, 4G, IMU and all of these other data sources from the vehicle in an intelligent way, in an affordable way.”
Indeed, one of the things that make ESCAPE unique is the way it brings together high-end GNSS processing capabilities with an industrialisation process that targets high volumes and comparatively limited cost and size. It also encompasses hardware and software safety procedures required for certification for the automotive market.
Garcia explained, “At Ficosa, we are a top-tier global provider devoted to the research, development, manufacturing of vision, safety, connectivity and efficiency systems for the automotive sector. We provide solutions directly to vehicle manufacturers. Based on our expertise and thanks to the work we have done on this project, we understand very well that GNSS is a central focus for a lot of applications. From the moment we started working on this project, at Ficosa we realised that this is a new and very important market. Right now we are working on a positioning system for autonomous driving based on this unit. This is part of our roadmap at the moment. This is a positioning system that we are ready to offer to the customer.”
The unit is ready now, but we have yet to see autonomous cars in large numbers on the road. Is this a problem for the ESCAPE system? Garcia answered, “From the very first moment that you have an autonomous car in the street, you will need high-accuracy positioning, because these vehicles will need this positioning to maintain themselves safely on the road. But we don’t have to wait for autonomous cars. The vehicles on the road today can already benefit from this technology.”
Garcia pointed to Europe’s eCall system, where a call centre automatically receives location information from vehicles in distress, thanks to on-board GNSS. “You already have this emergency call technology in the vehicles,” Garcia said, “and it provides a location, so the better the location is, the easier it is to locate the people in an emergency situation. No, we don’t have to wait.”
Location and more
One thing everyone seems to agree on is that autonomous vehicles will soon be appearing on European road networks, and most driving-related decisions will be based, one way or another, on the location of the vehicle and of other vehicles and objects in its vicinity. So vehicle location and positioning will be a critical component for the effective transportation of people and goods by self-driving road vehicles. That positioning will be enabled mainly by GNSS technologies, including Europe’s Galileo, which is expected to offer significant benefits in terms of accuracy and authentication compared to the other satellite-based navigation systems.
GNSS-based location will have to be complemented by other technologies in order to get to the integrity level needed in all driving situations, but the GSA also believes the combination of dual-frequency GNSS and 4G/5G connectivity can do more than just navigation, enabling as well a diverse range of in-vehicle location-based services (LBS), much like what we see emerging in smartphones. The EU-funded ESCAPE project, with its innovative GNSS engine, represents an important step forward in the pursuit of accurate, reliable and affordable positioning and connectivity for the emerging autonomous and connected cars markets.
My last column discussed the preliminary results of NGS’ second Multi-Year CORS Beta Solution of the National CORS. Since my last column, NGS announced the release of the beta version of the hybrid geoid model GEOID18 and, on Feb. 15, NGS officially released the Beta CORS ITRF2014 coordinates and velocities.
This column provides the official links to NGS website that provide the beta coordinates and information about the latest multi-year CORS solution. Below is the NGS announcement of the beta release of the updated coordinates.
Excerpt from Feb. 15, 2019, “NOTICE: New BETA Coordinates Available for CORS and OPUS”. (Screenshot: NGS)
NGS also provides a notice of the new beta coordinates on the National Geodetic Survey homepage, with a link to the Beta CORS ITRF14 coordinates (see the highlighted section below).
National Geodetic Survey homepage. (Screenshot: NGS)
Information on Multi-Year CORS Solution 2. (Screenshot: NGS)
By clicking on the CORS Home button, the user is directed to the Beta CORS page.
Beta CORS release page. (Screenshot: NGS)
This page clearly states that the ITRF2014 reference frame for CORS is available as a beta product. It also implies that these coordinates are being used in other beta products such as OPUS. I’ll address this later in this column.
Users can obtain information about the MYCS and other related products and services such as Beta OPUS by clicking on links provided on the Beta CORS homepage.
Accessing information about ITRF2014 frame in NGS beta products. (Screenshot: NGA)
It should be noted that these values are considered “beta” and are available to users for testing and feedback. NGS provides a statement about its beta release products. Basically, it states that users should only use beta products to test their workflows and never for official or production work.
The NGS beta release statement. (Screenshot: NGS)
To facilitate testing of the beta CORS coordinates and velocities, NGS provides links to other beta products that will use the MYCS 2 coordinates and velocities.
By clicking on the link labeled BETA OPUS on the beta CORS homepage, the user is directed to the BETA OPUS webpage. This page clearly states that the beta OPUS routine uses the new ITRF2014 reference frame for CORS.
Once again, the Beta OPUS Projects website clearly states that the beta version is using the CORS coordinates and velocities from the MYCS2. It also states that, at this time, NGS will not accept ITRF2014 submissions for publication. As previously stated, NGS’ beta products are for users to test their workflows and should never be used for official or production work.
The Beta CORS webpage provides a lot of valuable information on the processing and establishment of the multi-years CORS solution. I’ve highlighted several of the sections below.
First, by clicking on the link MYCS2 Processing, the user is directed to the section that describes the data used and the processing strategy.
Excerpt from Beta CORS Webpage – MYCS2 Processing. (Screenshot: NGS)
The following are highlights from the section:
The processing included data spanning 1996 to 2016 and involved around 3050 CORS, IGS and other (e.g., NGA) stations.
The corresponding input and output data occupied about 25 TB on the NGS computers.
The residual time series in the early 1990s showed exceptionally noisy behavior at times, which were deleted in the alignment/velocity computation stage.
The processing was performed in 3 steps:
1. The global processing step solves for orbits, Earth Orientation Parameters (EOPs), hourly tropospheric delay parameters and weekly global (IGS) station positions in an IGS-NNR frame.
2. The CORS processing step ties the remaining CORS to global, backbone, sites holding fixed estimated orbits, troposphere, EOPs and IGS station coordinates. This leads to estimated CORS coordinates in a no net rotation (NNR) frame.
3. The last step is the alignment of the estimated coordinates with ITRF2014 and velocity estimation. This process was done in 15 iterations to achieve rigorous quality control and discontinuity detection.
Linear velocities for all stations are estimated in the NGS realization of ITRF2014. NGS explains how this was implemented in the section titled “The velocity field relative to ITRF2014” (see box titled “Section Describing the Velocity Field Relative to ITRF2014”). The website provides figures that depict the horizontal and vertical velocities used in the processing.
The following are a few highlights from the section:
Unless an earthquake or a post seismic adjustment occurred, the velocities of a station in between discontinuities are constrained to have the same value.
Stations that experience earthquakes, post seismic adjustment and in a few cases, non-uniform vertical motion, are allowed to have different velocities in between events as dictated by the data.
The webpage provides figures that depict the estimated horizontal and vertical CORS velocities.
Section describing the velocity field relative to ITRF2014. (Screenshot: NGS)
What users usually want to know is how much the coordinates have changed and what it means to their surveying activities. The section titled “Main Changes Compared to Previous Reference Frames” provides information and plots that depict the changes of coordinates.
Section on changes in coordinates. (Screenshot: NGS)
This section provides NAD83 (MYCS2) coordinate values minus NAD83 (MYCS1) coordinate values.
The following are a few highlights from the section:
The ITRF2014 coordinates of all computed CORS coordinates from MYCS2 processing are converted to NAD83 (2011) using HTDP.
The resulting NAD83 (2011) coordinates are then compared to those obtained from MYCS1 at all common sites.
The coordinate differences are compared at epoch 2010.0 (MYCS2 – MYCS1).
The differences are less than 5 mm in most areas with some exceptions.
The largest differences are seen in southern Alaska.
Other visible changes are seen in areas of significant and real subsidence and in places where the time series are too short, such as in Iowa where almost all time series are three years long.
Vertical coordinates (ellipsoidal heights) are compared using the same criteria.
The stations with the HTDP estimated velocities from MYCS1 (no vertical velocities) show the largest differences. In addition, non-secular subsidence areas also show larger differences.
By clicking on the plots, the user is directed to a larger figure that is easier to interpret. (See boxes titled “NAD83 (MYCS2) – NAD83 (MYCS1) Horizontal Position Differences” and “NAD83 (MYCS2) – NAD83 (MYCS1) Vertical Position Differences.”)
NAD83 (MYCS2) – NAD83 (MYCS1) Horizontal Position Differences. (Screenshot: NGS)NAD83 (MYCS2) – NAD83 (MYCS1) Vertical Position Differences. (Screenshot: NGS)
NGS has done a tremendous job of explaining the MYCS2 process and results. As the results indicate, most differences between the MYCS1 and MYCS2 are small. Saying that, I would encourage all users to look at the NGS Beta webpages and obtain an understanding of the MYCS2 process and results. Users should also use the beta products and compare their results to the current production products to evaluate the CORS beta coordinates and velocities in their region of interest.
Notice announcing beta version of Geoid18 on NGS homepage. (Screenshot: NGS)
It should also be noted that in late February, NGS released a beta version of the latest hybrid geoid model, Geoid18. This model can be accessed here; the site provides an opportunity for users to compute a beta Geoid18 value for a particular station.
Excerpt from beta Geoid18 website. (Screenshot: NGS)
I would encourage all users to obtain an understanding of the new hybrid model. Once again, it should be noted that this model is a beta model for users to test their workflows and should never be used for official or production work.
My next column will discuss the beta hybrid Geoid18 model, and the differences between the beta model and the official hybrid geoid model, Geoid12B.
Inertial 2019, the sixth annual Institute of Electrical and Electronics Engineers (IEEE) International Symposium on Inertial Sensors and Systems, took place in Florida earlier this month. Events of particular note included two keynote talks from experts at the U.S. Defense Advanced Research Projects Agency (DARPA) and the Air Force Institute of Technology (AFIT), and a technical paper on the “Design and Performance of Wheel-mounted MEMS Inertial Measurement Unit (IMU) for Vehicular Navigation.”
Miniature Sensors. Ronald Polcawich from DARPA addressed “Miniature Navigation Grade Inertial Sensors: Status and Outlook.” The agency’s Precise Robust Inertial Guidance for Munitions (PRIGM) program has focused for more than three years on developing inertial sensor technologies to enable PNT in GPS-denied environments. PRIGM has developed a navigation-grade inertial measurement unit (NGIMU) based on micro-electromechanical systems (MEMS) platforms. The device has a mechanical/electronic interface compatible with drop-in replacement for existing tactical-grade IMUs on legacy U.S. Department of Defense (DoD) platforms.
PRIGM’s second main area of interest is advanced inertial micro sensor (AIMS) technologies for future gun-hard, high-bandwidth, high-dynamic-range, GPS-free navigation. It explores alternative technologies and modalities for inertial sensing, including photonic and MEMS-photonic integration, as well as novel architectures and materials systems.
Map-Matching. Aaron Canciani from AFIT educated the many computer scientists, software developers, information technology professionals, physicists and electrical and electronics engineering attendees on “The Importance of INS Accuracy for Map-Matching Navigation.”
The GPS-alternative technique matches measurements from a sensor to a map to provide navigation information. With repeatable measurements, almost any map may be used to navigate. Common maps used for navigation include terrain height, gravity, magnetic fields, Wi-Fi RSS and more. The inertial navigation system often plays a critical role in the accuracy of these methods, and increased INS accuracy plays a synergistic role in an overall map-matching navigation system.
WHEEL-MOUNTED IMUS
In today’s automobiles, MEMS gyroscopes and accelerometers provide essential measurements for enhancing stability and control. Both types of sensors have significant noise at low frequencies, limiting the measurement accuracy, particularly in low-dynamic conditions. Further, uncompensated accelerometer tilt causes large bias to acceleration estimates. For gyroscopes, physical rotation of the sensor can remove the constant part of the gyro errors and reduce low-frequency noise. In ground vehicles, such rotation occurs conveniently in wheels.
When inertial sensors are attached to the wheel, both types of sensors provide information on the rotation, gyroscopes naturally and accelerometers via specific force measurement. As a result of carouseling, accurate wheel heading, roll and pitch estimation can be estimated with high resolution, and the result is nearly bias-free. Combining the wheel orientation to distance traveled via known radius enables classic dead-reckoning mechanization (assuming zero slip) and other vehicle dynamics monitoring systems (considering wheel slip as unknown to be solved).
Authors Jussi Collin of JC Inertial Oy, Finland, and Oleg Mezentsev, Pacific Inertial Systems Inc., Canada, provided details of wheel-mounted inertial system hardware and algorithms and showed test results for several system configurations and applications. They discussed future system improvements — in particular, system miniaturization and an energy-harvesting development progress for next-generation inertial systems.
They have designed a wheel-mountable sensor system that contains MEMS sensors, battery, Bluetooth module and electronics to run computations and navigation algorithms on board. It operates in several programmable modes:
Computes navigation parameters real time and sends them via Bluetooth to an onboard computer (can be any other integrated system, data logger or a tablet).
Sends real-time raw data to an onboard computer.
Records high-rate raw sensor data (up to 2 kHz) to an embedded micro-SD card.
The onboard computer is a MEMS-array IMU with 48 gyro and accelerometer channels, with a BT receiving and sync controller, data storage and Wi-Fi interface. They can connect up to four such units to one onboard computer and have all their data in sync with the in-cabin inertial data. All of this data can be used for navigation, wheel dynamics measurements or road quality monitoring applications.
Trimble has awarded a significant in-kind gift to the Department of Construction Management at Colorado State University (CSU) that will expand the university’s leadership in training and research for 3D building design, construction management, digital fabrication, civil infrastructure, geomatics and the sustainable built environment.
The gift will enable CSU to integrate across its curricula Trimble solutions that are rapidly transforming how building and living environments are designed and constructed.
Trimble’s portfolio of building construction solutions support the Constructible Process, Trimble’s approach for enabling digital transformation of architecture, engineering and construction (AEC) workflows. This process empowers disparate teams across the construction lifecycle with actionable data to improve productivity and reduce waste.
The gift will be recognized as “Technologies by Trimble” throughout the Department of Construction Management.
Photo: Trimble
The department’s labs will include Trimble laser scanning, Trimble Field Link and Rapid Positioning Systems, UAS and surveying systems, and GNSS receivers. Trimble’s software packages will include RealWorks scanning software, Trimble Business Center, Vico Office Suite, Tekla Structures, Sefaira Architecture and its 3D modeling software SketchUp Pro, along with MEP software such as AutoBid SheetMetal and Mechanical, Sysque and AccuBid Electrical estimating packages.
Potential applications of these technologies include scanning historic and other buildings to ensure their preservation as well as planning future renovations; designing and 3D printing of architectural building models; surveying and layout; and improving construction estimating and scheduling to reduce costs.
“Working with Trimble represents the culmination of a fruitful, multi-year collaboration between CSU’s Department of Construction Management and Trimble,” said Jon Elliott, assistant department head and undergraduate program coordinator in the Department of Construction Management.
“Through numerous pieces of Trimble hardware and software applications, students gain important exposure to cutting edge technologies in surveying, virtual design and construction (VDC)-based estimating, site logistics, 3D modeling, building energy performance analysis, laser scanning, photogrammetry, and so on.
“Beyond the applications, Trimble’s dedicated employees provide outstanding educational opportunities through software demonstration and training. Through this exciting collaboration, Trimble is making significant contributions to our goal of preparing construction management students for a technologically advanced and dynamic construction industry.”
“Collaborating with CSU’s Department of Construction Management has been exciting. Trimble’s portfolio is highly relevant for students at the university,” said Roz Buick, Trimble vice president. “It will be rewarding to see the next generation of architecture, engineering, construction and building operations professionals experience the breadth and depth of our construction lifecycle solutions. We also look forward to supporting and learning from these new professionals as they experience and apply our solutions to real-world applications in their curricula.”
The gift was made to CSU’s Construction Management Program in the College of Health and Human Sciences.
The contract marks an important step in the long-term partnership between SSC and Airbus, and extends the capabilities of both companies.
The first two very high-resolution Pléiades Neo satellites will be launched in mid-2020, followed by a second pair in 2022. They will join the existing Airbus constellation of optical and radar satellites, and will offer enhanced performance, and the highest reactivity in the market.
SSC will provide comprehensive ground segment support for the Launch and Early Orbit Phase (LEOP), as well as routine on-orbit support for Telemetry, Tracking and Control (TT&C) and data reception.
Ground Network. The core SSC ground network for Pléiades Neo will consist of the unique dual polar ground station solution of Kiruna, Sweden, and Inuvik, Canada — often referred to as “Kinuvik” as it is operated as a virtual single polar station.
The partnership also includes an option to provide potentially higher data volumes at a later stage, using the southern hemisphere station of Punta Arenas, Chile.
The optimized and highly resilient SSC ground network provides effective tasking and downloading of large data volumes more than once every orbit, enabling rapid delivery of Pléiades Neo data from anywhere on Earth.
The ground network has been designed by SSC and Airbus to complement Airbus’ Direct Receiving Stations (DRS) as well as the Airbus SpaceDataHighway relay satellite system, while being flexible to adapt to changing seasonal needs and to give critical network diversity.
“The Pléiades Neo constellation will be adding two million km² per day at 30-cm resolution to Airbus’ imagery offering. As tasking and downloading will be possible in every orbit, up to 60 times a day for the constellation, we need to rely on very efficient commercial polar communication services,” said François Lombard, head of Intelligence Business at Airbus Defence and Space.
“Pléiades Neo is a cutting edge very high resolution Earth Observation constellation, and this represents a huge milestone in the close cooperation between Airbus and SSC. We are proud to be able to support Airbus in providing such critical optical imagery for the global marketplace”, said Stefan Gardefjord, CEO at SSC.
Komatsu Australia and Skycatch Inc. are partnering to boost the efficiency of construction, mining and quarry sites across Oceana with the High Precision Package.
The High Precision Package is also known as Komatsu’s Everyday Drone Solution, a key component to the Smart Construction workflow.
The Everyday Drone contains the Explore1 high-precision UAV, the Edge1 integrated GNSS base station and edge compute module, and the Viewer, an online data visualization and analysis tool, packaged into a commercial-grade kit.
High-precision package. (Photo: Skycatch)
The Everyday Drone allows a user to experience time to data without needing ground control points (GCP), and the ability to seamlessly integrate precision aerial data into their Smart Construction Workflow.
In common construction workflows, the time to data using traditional surveying methods could take weeks until project stakeholders can view or analyze their job site data. With the Edge1, customers are able to leverage a seamless GCP free workflow that consistently delivers sub 50-mm accurate data, in arbitrary or local coordinate systems within 30 minutes, saving countless hours and labor costs.
“We are now using the Everyday Drone at the start of projects to collect whole site information for pre-tender and bidding capability, comparing against 3D design surface to provide fleet and project managers real, accurate information viewed in the Smart Construction Application,” said Aaron Marsh, national technology solution expert manager, Smart Centre, Komatsu Australia. “This allows them to work out their cut and fill volumes with accuracy from the beginning, and enables the project tender team to select the right machines for the project optimising fleet recommendation and empowering the team to make the right, data-based decisions from the start.”
“Skycatch is proud to offer a better way of accessing precision data on-site. With traditional methods, the solutions are piecemeal, cumbersome, and time-consuming,” said Christian Sanz, Skycatch founder and CEO. “Now, customers are able to make informed decisions about changes to what was planned and what is actually happening on site in near real-time, ultimately providing greater productivity, increased profit and reducing project risk from beginning to end,”
Customers tested the receptiveness of the Everyday Drone Solution, said James Mackenzie, national remote support manager at Komatsu Australia. They first tested in civil construction, and then quickly expanded into quarry sites. Mackenzie was able to survey six quarries in five days for different customers, post-processed in the cloud, receiving the data back the next day.
“Compared to a traditional survey, this is 100% more productive and efficient,” Mackenzie said. “By using the Everyday Drone, customers are no longer putting themselves in harm’s way, surveying around heavy machinery or climbing up unstable stockpiles at risk of twisting an ankle.”
He also noted that customers appreciated the fast turn-around time, the ease of use, and the ruggedness of the products.
“Skycatch’s ability to provide near real-time data throughout the entire project is vital, and being able to deliver that to the customer, supervisor and give project teams the ability to make decisions throughout the project easily with usable, accurate data is key to the success of the project as a whole, not just in siloed environments is priceless,” Marsh said.
Emlid, the creators of Reach, centimeter-accurate RTK GNSS receiver, is now taking pre-orders for its multi-band GNSS receiver Reach RS2. The new receiver features a built-in LoRa radio, a 3.5G modem, and a survey app for iOS and Android.
Photo: Emlid
L1/L2/L5 RTK GNSS receiver with centimeter precision. Reach RS2 determines a fixed solution in seconds and provides positional accuracy down to several millimeters. The receiver tracks GPS/QZSS (L1, L2), GLONASS (L1, L2), BeiDou (B1, B2), Galileo (E1, E5) and SBAS (L1C/A), and reliably works in RTK mode on distances up to 60 kilometers and 100 kilometers in PPK mode. A multi-feed antenna with multipath rejection offers robust performance even in challenging conditions.
RINEX raw data logs are compatible with OPUS, CSRS-PPP, AUSPOS and other PPP services so users can now get centimeter-precise results any place on Earth.
Built-in 3.5G modem and UHF LoRa radio. The Reach RS2 features a power-efficient 3.5G HSPA modem with 2G fallback and global coverage. The corrections can be accessed or broadcast over NTRIP independently, without relying on internet connection on a smartphone.
For remote areas, the Reach RS2 has a built-in LoRa radio that has proven to be a reliable link for RTK corrections for distances up to 8 kilometers.
Designed for Tough Conditions. The Reach RS2 is engineered to be waterproof and impact-resistant. Its body is manufactured in a two-step injection process and is made out of shockproof polycarbonate covered in a special elastomer for extra protection. The receiver has an industry-standard 5/8-inch mounting thread.
The LiFePO4 battery of the Reach RS2 is designed for 16 hours of work as a 3.5-G RTK rover on one charge regardless of weather conditions. It can charge from a USB wall charger or a power bank over USB-C.
A RS232 interface allows users to connect the Reach RS2 directly to external hardware and output position in NMEA.
Photo: Emlid
ReachView App. The Reach RS2 comes with a mobile app, ReachView for iOS and Android, that is used to control all the features of the device. Users can create projects, collect and stake-out points, and import and export geodata in industry-standard formats such as CSV, DXF and Esri Shapefile.
The Reach RS2 comes in a carrying bag with a USB-C cable and a LoRa radio antenna. The ReachView app is available for download from Play Market or App Store.
Shipping of the first batch starts in mid-June 2019.
Using Wavelets for a Robust Vector-Tracking-Based GPS Software Receiver
Innovation Insights with Richard Langley
ALFRÉD HAAR. Who is he, you might ask? Alfréd Haar was a Hungarian mathematician who introduced the concept of wavelets during his Ph.D. work on orthogonal functional systems under David Hilbert of Hilbert transform fame. And what is a wavelet? Generally speaking, a wavelet, as its name suggests, is a brief oscillation in time with an amplitude that begins at zero, goes through one or more variations, and returns to zero. It’s a bit like the cardiac cycle of each heartbeat shown on an electrocardiogram. But wavelets, unlike heartbeats, are mathematical functions with well-defined properties.
Although Haar initiated the use of wavelets back in 1909, it was not until the 1970s and 1980s that the study of the use of wavelets — wavelet analysis — was undertaken to help solve a variety of problems in science and engineering with new application areas springing up all the time. We’ll get to one of these new areas — GNSS jamming mitigation — in just a bit, but let’s discuss a more mundane application first.
Let’s say we have a digitized audio recording of Maynard Ferguson’s rendition of “MacArthur Park” in our computer. We could do a Fourier transform (related to the Hilbert transform mentioned earlier) of the entire recording, which would show us all of the specific audio frequencies making up the song. But what if we wanted to determine where in the song Ferguson played a particular high note, such as double high C (not his highest)? We could create a wavelet with that frequency and a short duration such as that of a 32nd note and use the mathematical operation of convolution (involving shifting, multiplication and integration) to find one or more spots in the recording with a similar frequency. We could extend the procedure and use a set or bank of wavelets to fully study the song in both frequency and time.
Wavelet analysis will work on many kinds of data, not just audio signals. With an appropriate set of wavelets, we could decompose the data without gaps or overlap, store the resulting product for further analyses and, if necessary, reconstitute the original data with minimal distortion. The U.S. Federal Bureau of Investigation uses wavelet analysis to store compressed digital versions of fingerprint images. A heavily damaged recording of Brahms playing one of his own compositions on an Edison wax cylinder was partially restored using wavelet analysis despite the music being immersed in noise. And the small effect of El Niños on the Earth’s rotation has been studied using wavelet analysis.
And, yes, wavelet analysis is helping to improve the use of GNSS. The tasks being undertaken include de-noising of pseudorange measurements, cycle-slip detection and elimination in carrier-phase measurements, and separating biases such as multipath from high-frequency receiver noise. In this month’s column (which, by the way, now appears four times per year), we learn about another GNSS application of wavelet analysis — specifically the use of the wavelet packet transform — to efficiently identify and separate a jamming signal from the combined signal in a GPS receiver. In a narrowband jamming test using a hardware simulator system, no positioning was possible with conventional receiver operation. But with the proposed approach, the jamming signal was readily suppressed, allowing the satellite signals to be acquired and a positioning solution to be computed. Thank you, Alfréd Haar.
GPS technology has been integrated into many aspects of our daily lives. Hence, there is a growing demand for a robust GPS receiver that can operate efficiently without external aiding to provide continuous, reliable and accurate positioning, navigation and timing (PNT) solutions. However, this is not always possible due to frequent loss or attenuation of signals, multipath or interference. In such challenging conditions, a system malfunction can cause safety problems, especially in health-critical applications.
Receiver architecture plays a major role in defining a receiver’s robustness against the challenges just mentioned. Scalar-tracking-based GPS receivers can achieve high navigation accuracy under line-of-sight (LOS) conditions. However, they always fail to provide adequate accuracy in signal-degraded environments such as urban, suburban and dense foliage environments. On the contrary, vector-tracking-based GPS receivers provide better performance in such challenging environments. In vector-tracking-based receivers, both the tracking loops and the navigation processor are combined to solve a single estimation problem. Hence, there are many advantages of this architecture over that of scalar-tracking-based receivers. First, information from strong signals from healthy satellites is used to track weak signals, when signals are highly attenuated or even totally blocked. Thus, vector-tracking-based receivers have better immunity to jamming and interference. Second, they can rapidly reacquire signals after a satellite outage. Third, they have an improved navigation solution accuracy compared to that of scalar-tracking-based receivers, even under normal LOS conditions. All of these advantages make vector-tracking-based receivers the best platform for our research on receiver robustness. However, vector-tracking-based receivers still suffer from degraded performance in the presence of strong jamming signals. Therefore, we are proposing a new anti-jamming technique to be employed for interference mitigation in vector-tracking-based GPS receivers.
The spread-spectrum nature of GPS signals provides resistance to narrowband interference due to the spreading and despreading processes that take place at the transmitter and receiver respectively. However, a GPS signal reaches the receiver with very low power on the order of –158 dBW, which makes it vulnerable to jamming. A jammer with enough power and suitable time and frequency properties can degrade the positioning solution accuracy and may cause a total blockage of the GPS signals. Besides, the presence of a jamming signal increases the challenge of acquisition of the desired GPS signal.
Therefore, many anti-jamming techniques have been employed for interference mitigation in GPS receivers. There are various anti-jamming methods for GPS receiving systems, which are mainly classified into four groups:
Antenna-level techniques, which are based on the use of antenna arrays to generate a radiation (reception) pattern that attenuates the interference signal based on the direction of arrival.
Automatic gain control (AGC), where interference can be detected by the saturation of the AGC.
Post-correlation techniques, which process the signals after passing through the correlators.
Pre-correlation techniques, which are based on processing the signals after passing through the analog-to-digital converter but before they get to the correlators.
This article introduces a novel interference mitigation technique based on the wavelet packet transform (WPT), which belongs to the pre-correlation techniques category. The WPT enables the received interfered combined GPS signal to be represented in a transformed domain in which an interference signal can be better identified and separated, without significant degradation of the useful GPS signal. The WPT has been extensively discussed in the literature in the framework of GPS and other GNSS. For example, wavelet multi-resolution analysis has been used in one study to remove the multipath error and leave the useful signal untouched. In another study, multi-resolution analysis using wavelets was applied to pseudorange and carrier-phase GPS double differences to reduce multipath effects. And in another, researchers developed a technique to detect and remove cycle slips based on wavelet multiresolution analysis.
The WPT has been widely used in the context of jamming to mitigate pulsed and narrowband interference. Although the WPT showed outstanding performance in jamming mitigation, the main drawback of this technique is the computational complexity. In this article, we introduce a novel wavelet packet-based technique for narrowband jamming mitigation with significantly reduced computational complexity.
Signal and Interference Models
The GPS signal employs a direct sequence spread spectrum communication technique, in which the signal is multiplied by a spreading or pseudorandom noise (PRN) code. As mentioned earlier, this spreading technique gives GPS some immunity to narrowband jamming. The received digitized spread spectrum signal at the output of the receiver’s analog to digital converter (ADC) can be represented by:
(1)
where, for signal s, ym(nTs) is the useful GPS signal received from mth LOS satellite, j(nTs) is the jamming signal, w(nTs) is additive white Gaussian noise (AWGN), M is the number of visible satellites, n is the sample number and Ts is the sampling rate.
The useful received GPS signal can be described as follows:
(2)
where P is the signal power, d(nTs) is the navigation data, c(nTs) is the spreading pseudorandom noise code, fIF is the intermediate frequency, no is the code delay, fd is the Doppler shift, and θo is the carrier phase.
Interference signals are classified based on their spectrum characteristics: narrowband or wideband depending on the signal’s bandwidth relative to the bandwidth of the desired GPS signal.
Our focus in this article is on the mitigation of narrowband interference, specifically a linear chirp signal. A chirp signal can be expressed as:
(3)
where a is the chirp signal amplitude, fo is the starting frequency, k is the sweeping frequency, and Tsw is the sweeping frequency period. The chirp is continuously repeated.
Wavelet Packet Transform
The wavelet packet transform or WPT is a class of transformed domain techniques that has been widely used in the context of jamming mitigation in GPS signal reception. It allows for the study of a signal in both time and frequency domains simultaneously. In the WPT, the signal is decomposed into approximations (the low-pass component) and details (the high-pass component) with respect to a group of local basis functions. These functions can be obtained through dyadic scaling and shifting of the so-called mother wavelet. The discrete wavelet basis functions are given by:
(4)
where j and k are integers, so is the dilation step, and τo is the scaling coefficient. The decomposition of the signal with respect to a scaling function acts as low-pass filtering of the signal, while the decomposition with respect to a wavelet function acts as high-pass filtering of the signal. The signal is then down-sampled, and this procedure is further iterated on all the sub-bands using scaled and dilated versions of the wavelet and scaling functions. This filtering process allows the decomposition of the GPS signal with respect to a local basis function, in which each of these sub-bands identifies a limited frequency band of the received signal, and the frequency resolution is dependent on the level of decomposition. The wavelet packet decomposition can be realized as a filter-bank as depicted in FIGURE 1.
As mentioned earlier, the main drawback of WPT is the time complexity. Due to the decomposition of both approximation and detail components, if the signal is decomposed into L levels, the resultant number of coefficients is 2L. For instance, if we used 10 decomposition levels, the resultant number of wavelet coefficients is 210 (1,024). However, as each wavelet coefficient component represents a limited portion of the frequency of the received signal, the jamming signal will only affect a few coefficients. Thus, the main idea of the proposed algorithm is to identify those coefficients that are affected by the jamming signal and reconstruct the jamming signal after denoising them. Then, the estimated jamming signal is subtracted from the received signal to obtain the jamming-free useful GPS signal.
Identifying the wavelet coefficients affected by interference is achieved by computing the median absolute deviation (MAD). As those coefficients that are affected by interference have a higher MAD value than those that are not affected, the decision of whether the wavelet coefficients are affected by interference is based on comparing their MAD values with a certain predefined threshold. This threshold is determined based on the desired detection and false alarm probabilities according to the distribution of the received signal samples in an interference-free environment. Only the sub-bands whose MAD values exceed the threshold are considered to be affected by interference and are further decomposed.
Therefore, only the sub-bands affected by interference are isolated and iterated. This technique allows for a considerable reduction in complexity, as both detection and mitigation can be applied in a limited number of sub-bands. FIGURES 2 and 3 show the tree decomposition of the received signal of two jamming scenarios based on the proposed algorithm. The frequency offset of the jamming signal from the GPS signal is 200 kHz in the first scenario and 600 kHz in the second one. The figures clearly illustrate the huge reduction in computational complexity as for 10 levels of decomposition; we ended up having only eight wavelet coefficients instead of 1,024.
FIGURE 2. Tree decomposition for scenario I. (Image: Authors)FIGURE 3. Tree decomposition for scenario II. (Image: Authors)
The proposed wavelet packet-based detection and mitigation algorithm is explained in three steps.
Decomposition Step. The incoming GPS signal is decomposed through a uniform filter bank by only one level. Then, MAD is computed for all the decomposed sub-bands. Only sub-bands with a MAD value greater than the predefined threshold will be further decomposed. This step is repeated until the maximum predefined decomposition level is reached.
Detection Step. The MAD value is computed for all sub-bands from the last decomposition level. Only sub-bands whose MAD value exceeds the predefined threshold are considered affected by interference and are used to reconstruct the jamming signal using the inverse wavelet transform.
Reconstruction Step. In this step, the useful GPS signal is reconstructed free of interference by subtracting the estimated jamming signal from the received signal.
Experimental Work
In our investigation, a GNSS simulation system was used to create a fully controlled environment to examine and validate the performance of the proposed method using semi-real simulation scenarios. The simulator was controlled by simulation software that enables the simulation of multipath reflections through an advanced multipath model as well as atmospheric degradation to signals and the effects of antenna patterns and terrain obscuration. Moreover, it can generate simulated land, air, space and sea trajectories. Furthermore, the simulator when connected to an interference simulation system can provide various controlled jamming environments using an interference signal generator. The full setup is shown in FIGURE 4.
The receiver used in this research is a prototype of a digital front end. The front end collects the output radio frequency (RF) signal from the simulator. Then, the RF signal is down-converted to baseband through several down-conversion stages, generating the in-phase (I) and quadrature-phase (Q) data. Then, the data is sampled and quantized through the ADC. The front end collects GPS L1 signals at different bandwidths ranging from 2.5 MHz to 20 MHz with quantization levels ranging from 1-bit to 8-bit. After that, the sampled digitized signal is sent to the computer via an Ethernet connection.
The raw I/Q GPS samples are then processed by a GPS software receiver. Our proposed algorithms have been implemented using Matlab by modifying the open-source GPS software-defined radio (SDR) receiver composed by Borre and Akos, which is widely used in research.
To verify the performance of the new proposed algorithm, a full GPS C/A-code signal was simulated using the previously mentioned simulation system. A static simulated scenario was generated for this purpose. This static scenario was run twice, once in an interference-free environment for reference, and one where the jamming signal was enabled. The simulation, front end and SDR receiver parameters are shown in TABLE 1.
Table 1. Data collection and processing parameters. (Data: Authors)
FIGURES 5 and 6 show the power spectral density (PSD)of the received signal before and after applying the proposed jamming mitigation technique. The figures demonstrate that the interference components have been highly attenuated. To confirm the benefits of the proposed technique, the reconstructed useful GPS signal has been acquired using the SDR receiver.
FIGURE 5. PSD before jamming mitigation. (Image: Authors)FIGURE 6. PSD after jamming mitigation. (Image: Authors)
FIGURE 7 shows that the receiver is in a total blockage as it failed to acquire any satellite before applying the jamming mitigation technique. However, FIGURE 8 shows that the proposed algorithm allowed the retrieval of seven satellites.
FIGURE 7. Acquisition results before jamming mitigation. (Image: Authors)FIGURE 8. Acquisition results after jamming mitigation. (Image: Authors)
FIGURE 9 shows the cross-ambiguity function (CAF) of PRN31 before jamming mitigation. It is obvious from the figure that the search space is quite noisy, and the receiver fails to acquire the GPS signal due to the difficulty of isolating the peak from the noise. However, FIGURE 10 shows that the peak clearly emerges from the noise floor and can be easily detected by the receiver after applying the jamming mitigation algorithm.
FIGURE 9. CAF of PRN31 before jamming mitigation. (Image: Authors)FIGURE 10. CAF of PRN31 after jamming mitigation. (Image: Authors)
These figures demonstrate the power of the proposed algorithm and confirms that the useful signal is not lost during the filtering process. Before applying the jamming mitigation algorithm, the receiver lost lock on all satellites and failed to provide a navigation solution. However, after applying the proposed algorithm, the navigation solution is available with an accuracy of about 10 meters in the east and north components and around 20 meters in the up component, as shown in FIGURE 11.
FIGURE 11. Navigation solution. (Image: Authors)
Conclusion
In this article, we have proposed a new method for mitigating a linear chirp narrowband jamming signal based on the WPT. Although the WPT has been widely used in the literature in the context of mitigating narrowband jamming, this technique is characterized by a significant computational complexity that is not only proportional to the length of the signal, but also proportional to the wavelet decomposition level.
The results show that our proposed algorithm is able to maintain excellent performance in the suppression of the jamming signal with a significant reduction in complexity. In the proposed technique, the sub-bands affected by interference are identified and are further decomposed to be used to reconstruct the jamming signal. Then, the useful GPS signal is obtained by subtracting the estimated jamming signal from the received signal. The performance of the algorithm has been assessed with respect to acquisition and navigation performance. The results show that the proposed algorithm successfully suppressed narrowband jamming without significantly degrading the useful GPS signal.
Acknowledgments
This article is based on the paper “A Novel Wavelet Packet-based Jamming Mitigation Technique for Vector Tracking-based GPS Software Receiver” presented at ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018. The research was supported by the Natural Sciences and Engineering Research Council of Canada.
Manufacturers
The simulation system used a Spirent Communications Inc. GSS6700 Multi-GNSS Constellation Simulator, a Spirent GSS8366 Interference Combiner Unit and a Keysight Technologies N5172B-503 Interference Signal Generator. The receiver front end used was a NovAtel Inc. FireHose D17088 prototype digital GNSS front end.
HAIDY Y. ELGHAMRAWY is a Ph.D. candidate in the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada. She received her M.Sc. degree in engineering physics and mathematics from the Faculty of Engineering, Cairo University, Egypt.
MOHAMED YOUSSEF is leading GPS/GNSS product development activities for Sony North America. He holds an interdisciplinary Ph.D. degree from the Department of Geomatics Engineering and the Department of Electrical and Computer Engineering, University of Calgary, Canada.
ABOELMAGD M. NOURELDIN is a cross-appointment associate professor in the Departments of Electrical and Computer Engineering at Queen’s University and the Royal Military College (RMC) of Canada in Kingston. He is the director of RMC’s Navigation and Instrumentation Research Laboratory.
Since 1990, the urban population of Africa has doubled, with more than 80 percent of its denizens living in urban areas. Urbanization can contribute to sustainable growth, if managed well.
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The Global Human Settlement Layer from the European Commission’s Joint Research Centre (JRC) is a complete, consistent, global, free and open dataset on human settlements, and helps to quantify and understand the issues that drive urbanization. The above example comes from the JRC in its Esri story map “Building Knowledge for Sustainable Development in Africa,” which shows how the JRC contributes to the African Union (AU)-European Union (EU) partnership.
A roundup of recent products in the GNSS and inertial positioning industry from the March 2019 issue of GPS World magazine.
MEMS INS/GPS
Update improves heading performance and reduces jitter
Photo: Systron Donner
The SDN500 digital quartz micro-electro-mechanical systems (MEMS) GPS inertial navigation system (GPS/INS) has been updated. Model SDN500-xE provides a newer generation JF2 (C/A) code GPS receiver and tightly couples the 1 PPS GPS signal to the SDI505 IMU synch pulse to improve heading performance and reduce jitter after long periods of operation without dynamic inputs. The 25-inch-square SDN500 provides for maximum packaging flexibility in dense systems and delivers accuracies to within 1.0 mrad in attitude, 0.1 m/s in velocity and 3.9 meters spherical error probability (SEP). It offers tactical-grade performance, integrating SDI’s latest quartz gyros capable of 0.5°/hr. bias in-run stability and low angular random walk (ARW, 0.02°/√ hr), quartz accelerometers delivering 0.5 milli-g in-run bias stability and low velocity random walk (VRW, 80 µg/√ Hz), plus high-speed digital signal into a tightly coupled GPS/INS for tactical navigation and geo-location applications.
Testing location accuracy performance of mobile devices
Photo: Rohde & Schwarz
To create test concepts for over-the-air (OTA) antenna measurements, Rohde & Schwarz and Bluetest have integrated the R&S LBS Server, a software component running on the R&S CMW500 wideband radio communication tester, and the Bluetest over-the-air (OTA) test solution for A-GNSS systems based on Bluetest’s RTS65 reverberation chamber and Bluetest’s Flow measurement software. The R&S LBS Server controls the Rohde & Schwarz base-station simulator R&S CMW500 for LTE, WCDMA and GSM, and uses the R&S SMBV100B vector signal generator for simulation of GNSS and metropolitan beacon systems (MBS) signals. An upgrade for 5G will be available soon. The R&S LBS Server is an essential part of the R&S TS8991 OTA Performance Test System.
Brings Navsight technology to the most demanding environments
Photo: SBG Systems
The Horizon IMU adds a third choice to SBG System’s Navsight Land/Air Solution. It is a FOG-based high-performance inertial measurement unit (IMU) designed for highly demanding surveying applications such as high-altitude data collection or mobile mapping in dense areas such as urban canyons. The Horizon IMU joins the Ekinox and Apogee IMUs as options for Navsight. The different levels of accuracy enable the solution to meet various application requirements and can be connected to various external equipment such as odometer, lidar and more. The Horizon IMU allows customers to use Navsight in high-altitude and highly dense areas, as well as where only a single antenna can be used. It is based on closed-loop FOG technology that enables ultra-low bias and noise levels. The Navsight solution can be installed in a plane or car — the sensor alignment and lever arms are automatically estimated and validated. The Navsight unit also integrates LED indicators for satellite availability, real-time kinematic (RTK) corrections and power. Qinertia post-processing software provides access to offline RTK corrections from more than 7,000 base stations in 164 countries.
The Coach 4×4 Wi-Fi/DSRC GNSS multi-band antenna enables smart grids, mobile workforce communications, and advanced automation technologies. The dual-band 802.11ac/p MIMO antenna helps boost data rates and reliability for utility networks, intelligent transportation systems and other industrial internet of things (IoT) applications. The low-profile antenna features four-port 2.4/5-GHz coverage along with PCTEL’s high-rejection GPS/GLONASS technology for network timing and tracking in a single IP67-rated housing.
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