Septentrio has launched the Mosaic high-precision GNSS receiver module.
Despite its compact size (31 x 31 x 4 millimeters, 1.29 x 1.29 x 0.15 inches), the Mosaic module supports more than 30 signals from all six GNSS constellations, L-band and various satellite-based augmentation systems, the company said.
As a multi-band module tracking all GNSS satellites in view, it is also designed to support future GNSS signals.
It also supports correction services, and uses real-time kinematic (RTK) technology, together with Septentrio’s algorithms, to guarantee maximum accuracy and availability. The surface-mount design of Mosaic is optimized for automated assembly and ease of integration, with a full library of well-documented and flexible interfaces.
“Our new Mosaic module represents the best-in-class option for reliable and scalable position accuracy, with integrity,” said Chris Lowet, product manager at Septentrio. According to Lowet, it provides RTK positioning with a power consumption of 0.6-1 W, and requires no or minimal additional components for the design-in. “These characteristics make it an ideal positioning cornerstone for a variety of mass market UAV, autonomous and robotics applications,” Lowet said.
Photo: Septentrio
Robustness to interference. Due to the natural weaknesses of distant GNSS signals and a crowded radio-frequency spectrum, GNSS-based services are vulnerable to unintentional radio-frequency interference (RFI). They are also vulnerable to intentional RFI, attacks intended to disrupt receivers by means of counterfeit GNSS-like signals (known as spoofing), and to intentional transmission of RF energy to mask GNSS signals with noise (known as jamming).
To defend against these threats, Mosaic features Septentrio’s AIM+ technology. AIM+ can suppress the widest variety of interferers, from simple continuous narrowband signals to complex wideband and pulsed jammers, the company added. In addition, the integrated spectrum analyzer allows the RF environment around any Mosaic module to be viewed in real time in both time and frequency domains.
Effective interference countermeasures against threats to GNSS signals also require constant knowledge of the changing RF environment. The Mosaic module helps analyze these threats by continuously and automatically monitoring the GNSS frequency spectrum to detect, characterize, log and mitigate interference events when needed.
Sokkia’s Tammy Alto shares key highlights of the company’s new GRX3 fully integrated dual-constellation RTK GNSS receiver. Sokkia debuted the receiver at Intergeo 2018, which took place Oct. 16-18 in Frankfurt, Germany.
When a Pennsylvania county’s 911 system suddenly went down without warning, garbled messages across the network impacted fire and police agencies’ ability to respond to emergency messages. The issue was traced to a firmware malfunction on communications equipment, related to provision of GPS timing. The firmware had not been updated for 19-1/2 years. Why should it have been? Everything was working fine — until it didn’t.
Test lab set-up. Photo: Orolia
In addition to increased jamming and spoofing threats, GPS has a “week rollover event” set to happen in April 2019. If the GPS receivers found at the heart of many critical systems do not handle this properly, any number of failures can occur.
Without GPS timing, everything slows down, has less capacity and becomes more dangerous.
This Thursday, a complimentary webinar outlines test plans for GNSS equipment used in critical timing applications, discusses the need for assured access to accurate timing across financial institutions, industrial automation, telecommunications, transportation, the power grid and elsewhere — and defines just what “assured” access means and how crucial the “assured” part is — and finally reviews some recent mishaps and near-disasters caused by interrupted or inaccurate timing.
Speaking on the 1-hour webinar are Lisa Perdue, product manager and applications engineer, Orolia; Stefania Römisch, leader, the Atomic Standards Group at the National Institute of Standards and Technology; and Dana Goward, president, Resilient Navigation and Timing Foundation.
Following each speaker’s 12- to 15-minute slide presentation, a live Q&A period with the audience will explore particular issues and concerns.
A free lesson for those in charge of critical infrastructure systems such as the power grid, communications, financial markets, emergency services, and industrial control.
Many of these systems have functioned smoothly and efficiently for years, thanks to the precise timing provided by GPS receivers. That could change, suddenly and without warning, if predictive and preventative steps are not taken.
The GPS receivers somewhere near the hearts of these critical systems, if not thoroughly vetted, tested and checked for up-to-dateness, could constitute a vulnerability — a vulnerability that would be catastrophically exposed on April 6, 2019. In 6 months’ time.
Image: Orolia
The GPS constellation transmits the proper date and time to all receivers, worldwide, by supplying the current week and the current number of seconds into the week. This enables the receiver to translate the date and time into a more typical format: day, month, year, and time of day. Infrastructure systems use the precise timing to synchronize many complex operations across their respective networks. Critically, the field that contains the week number is a 10-bit binary number. This limits the range of the week number to 0 – 1023, or 1024 total weeks.
GPS week zero started January 6, 1980. The 1,024 weeks counter ran out and rolled over on August 21, 1999. The week counter then reset to zero, and it has been recounting ever since. The next time the counter will reach week 1,023 and roll over to zero is on April 6, 2019.
If the GPS receiver is new or has received firmware updates, it can accommodate and adjust for this change. But do you know for sure? Only if you test. Otherwise, your critical systems may go into a time warp, 19.7 years out of date. Visualize that discrepancy rippling outward from the core component of a critical timing system throughout your infrastructure. Or, simply not working at all.
It is incumbent upon all managers to verify that such an issue will not occur — well before its possibility arises. At a minimum, experts recommend consulting your receiver manufacturer to confirm that the issue has been fully tested and will not occur. Many manufacturers have already issued compliance statements, and are expected to continue doing so over the next year, up until the event occurs.
To be sure that your system will not experience any failures related to this issue, it is possible to test for this event using a GPS/GNSS simulator. The requirements for the simulator are straightforward. The basic yet key information necessary to undertake such testing will be communicated in a free webinar on Thursday, November 15.
The panel of expert speakers includes Lisa Perdue, product manager and applications engineer, Orolia; Stefania Römisch, leader, the Atomic Standards Group at the National Institute of Standards and Technology; and Dana Goward, president, Resilient Navigation and Timing Foundation.
You may register for this free webinar here, to attend it live or download it for later viewing at your convenience.
Here is a useful reference from the last time the rollover occurred, with a mention of the next one.
Photo: Technical University of Eindhoven
Eindhoven, the Netherlands, is home to the Eindhoven University of Technology, an incubator for technology startups where many scientists active in GPS research and in the direction of the Galileo satellite navigation program have trained.
Tectonics is the study of plates in the Earth’s crust that move in different directions and speeds. To study plate motion, GPS instruments are anchored firmly in bedrock to measure how it moves, infinitesimally yet measurably, thanks to the nanosecond timing provided by the GPS constellation and interpreted by properly calibrated and updated instruments.
A GPS/inertial trajectory data simulation podium can generate simulation data sets for all levels of GPS/INS integration. Here it verifies the operation and performance of a new ultra-tight GPS/INS integrated system, adaptable for both software and conventional hardware receivers.
Navigation systems for land vehicles, embedded in passenger cars, ambulances, police cars, fire trucks and others, provide reasonable accuracy in open-sky environments, but under conditions such as underpasses and tunnels GPS satellite signals cannot be readily tracked since they are not consistently available or have low signal power. One major factor that directly impacts the effectiveness of receivers in terms of complexity and speed is receiver architecture.
Scalar (conventional) signal tracking architectures process each satellite signal individually: pseudoranges and pseudorange rate measurements are produced separately and only combined in the navigation filter to generate the required solution. Hence, no information exchange happens between the different tracking channels.
On the contrary, vector tracking systems combine all the channels in one system along with the navigation filter to produce pseudoranges, pseudorange rates and the navigation solution all at the same time. Figure 1 shows the general architecture of a vector tracking system. Vector-tracking architectures have proven themselves able to provide better performance over scalar tracking systems in challenging environments where most satellite signals are received at low signal-to-noise ratios (SNR).
Figure 1. General view of the vector-based signal tracking system. (Image: Authors)
Any information available about the satellite constellation and user position and dynamics can be used to predict the received signals. Therefore, the best estimation we have for the receiver position and dynamics makes the vector tracking loops more robust. One approach to reduce or perhaps remove the receiver dynamic stress in the signal tracking loops is to provide external aiding information.
Several sensor types have been augmented with GPS to improve navigation system accuracy and reliability. The most common systems that have been widely augmented with GPS are inertial sensor systems (INS). Because an INS system can provide a continuous solution at a high data rate, it is virtually a twin to the GPS with respect to its widespread use in navigation applications.
Using the solution obtained from INS, one can estimate a line-of-sight acceleration that can be integrated to obtain a line-of-sight velocity. Car odometers also provide reasonably accurate measurements of the vehicle speed. Incorporating this velocity (from INS or other aiding sources) into tracking-loop computations helps the tracking loop to maintain tracking at a lower bandwidth even when high dynamics are experienced at the receiver. When the aiding source to the GPS signal tracking loops is an INS, the system is known as ultra-tight GPS/INS integration. Figure 2 shows a general block diagram of an ultra-tightly coupled GPS/INS integration system.
The ultra-tight GPS/INS integrated system enhances a GPS receiver’s tracking ability in challenging environments and consequently improves navigation availability.
Loose. The loosely coupled integration mode is easier to implement since the inertial and GPS navigation solutions are generated independently before being weighted together in a separate navigation filter. The advantages of this coupling strategy are that the INS errors are bounded by the GPS updates, the INS can be used to bridge GPS updates, and the GPS can be used to help calibrate the deterministic parts of the inertial errors instantly. The main drawback of this strategy, however, is that it requires at least four satellites in view which cannot always be guaranteed because of signal interruption due to many factors such as signal blockage by trees or tall buildings.
Tight. The tightly coupled integration mode combines both systems into a single navigation filter. The major limitation of visibility of at least four satellites is removed since this integration mode can provide a GPS update even if fewer than four satellites are visible. The tightly coupled architecture also overcomes the problem of correlated measurements that arises due to cascaded Kalman filtering in the loosely coupled approach. However, these advantages come with the penalty of increased system complexity.
Ultra-tight. In the ultra-tightly coupled integration approach, the raw measurements come from one step further towards the front end of a GPS receiver, in the form of I (in-phase) and Q ( quadrature ) signal samples. These I and Q measurements are integrated with the position, velocity and attitude of the INS in a complementary filter. The integration of INS-derived Doppler feedback to the carrier tracking loops provides a vital benefit to this system; the INS Doppler aiding removes the vehicle Doppler from the GPS signal. Hence, it results in a significant reduction in the carrier tracking loop bandwidth. In addition, due to lower bandwidths, the accuracy of the raw measurements is further increased.
The proposed method uses a variant of the Kalman filter as the core of the navigation processor coupled with the inertial sensor’s input in a reduced inertial sensor system (RISS) configuration and car speed odometer; see Figure 3. Additionally, the data sets used in this work are generated using a newly composed GPS/INS trajectory data simulation platform.
Figure 3. Reduced inertial sensor system (RISS). (Image: Authors)
Secondly, it demonstrates a novel GPS/INS trajectory data simulation podium. This combined simulation system can produce simulation data sets for all levels of GPS/INS integration and is used to verify the operation and performance of the ultra-tight GPS/INS integrated system.
SYSTEM ARCHITECTURE AND IMPLEMENTATION
The goal of signal tracking loops is to monitor changes in the main signal parameters, namely, code phase and carrier frequency, to keep the locally generated signal aligned with the received signal. Successful tracking of these variables will provide good estimations of the parameters that are required for the navigation filter to function correctly. Errors in the code phase and carrier frequency are usually represented as:
(1)
(2)
where and are the measured and estimated code phases, respectively. and are the measured and estimated carrier Doppler frequencies, respectively. These estimated errors at the signal tracking stage are directly linked to the errors in the states at the navigation filter.
Each tracking channel provides its own measurements based on a discriminator’s output. All the measurements are then processed together in the navigation filter and feedback is provided to each channel based on the obtained navigation solution results. The filter will process the error signals received from the discriminators in the form of code phase error and frequency error . Thus, the measurements of the filter will be pseudorange errors and pseudorange rate errors.
(3)
(4)
Where fcode is the code frequency = 1.023 x 106 Hz, fcarrier is the nominal L1 frequency = 1575.42 MHz, and η represents the measurement noise vector.
The computations of the navigation solution start with a mechanization process where we first calculate pitch, roll and azimuth angles. Knowing the Azimuth and pitch angles, vehicle forward velocity can be projected into East, North and Up velocities. The East and North velocities are transformed into geodetic coordinates and then integrated over the sample interval to obtain positions in latitude and longitude. The vertical component of velocity is integrated to obtain altitude. At this stage, we run the Kalman navigation filter through its two-step known cycle, prediction and update, incorporating any available measurements to estimate the receivers’ new position and velocity. Then, the estimated pseudoranges and pseudorange rates are calculated. Finally, the computed code and carrier frequencies are fed back to control the code and carrier oscillator inputs to align the locally generated signal with the incoming signal.
COMBINED SIMULATION SYSTEM
In our work, we combined two existing INS and GNSS simulators to build a comprehensive simulation tool that can produce a limitless number of data sets of repeated trajectories under entirely controlled circumstances. Moreover, these data sets can be used for any level of GPS/INS integration system validation. The system is also used to verify the performance of the above proposed ultra-tight GPS/INS integration system architecture.
For the GPS data, a satellite navigation simulation signal generator was used to build and generate the desired trajectory. The selected model has the ability to provide dynamic capacity in Doppler and signal power levels as well as adequate channels to simulate line-of-sight and multipath satellite signals. The unit is driven by a software package that comes in different versions; the most powerful version is used in this research to drive the simulation hardware system to generate the output radio frequency (RF) signal.
A receiver front-end then generates the discretized data stream in the form of in-phase (I) and quadrature-phase (Q) signals. The unit is a rugged dual-frequency L1/L2 front-end intended mainly for software receiver and interference detection systems. The unit is capable of logging L1/L2 data at bandwidths of 2.5 MHz, 5.0 MHz, 10 MHz and 20 MHz with data quantization varying from 1 bit to 8 bits.
For the INS data sets, the INS simulator, developed by the Mobile Multi-sensor Group at the University of Calgary, is used for simulating inertial measurement unit (IMU) raw data. The INS simulator can virtually generate the raw data measurements of any grade of IMUs such as navigation, tactical and consumer-grade systems. A wide number of sensor errors can be simulated using this software such as bias instability, random walk, scale factor, errors due to thermal drift and g-sensitivity and so on. While the simulator can generate raw IMU measurements using user-defined vehicle motion and dynamics, such as static scenarios, straight line, constant velocities, accelerations, turns and bumpy roads, and it can also accept externally injected vehicle dynamics from real trajectory data.
Figure 4 shows a high-level diagram of the trajectory data flow from the two arms of the synthesized simulator. Several conversion code scripts were written to convert raw data into the implementation platform workspace format. Both data sets were then merged through the implemented algorithm to provide the navigation solution.
Figure 4. Data simulation tool flow diagram. (Image: Authors)
Step 1 of Simulation Process. The trajectory design, Figure 5, outlines the general aspects of the process. Among these are the type of platform to be simulated, for example. land vehicles, ships, aircraft and so on; the satellite constellation, typically GPS, Galileo or GLONASS; the environment, whether rural, suburban or urban; and error sources, including ionospheric and tropospheric effects. All of this is done using the simulator’s software.
Figure 5. Trajectory data flow Step 1. (Image: Authors)
Step 2. This incorporates the implementation of the data stream that is fed into the signal generator hardware, which transforms this into an RF signal (Figure 6). Concurrently, the reference trajectory data is logged on the same computer that hosts the simulation software. The I and Q branches are recorded, simultaneously with the reference trajectory, on a GNSS receiver front-end.
Figure 6. Trajectory data flow Step 2. (Image: Authors)
Step 3. Finally, the inertial data is simulated. First, the INS simulator is configured according to the desired simulation parameters. Among these are the sensor data rate, grade (or quality) of the selected sensor(s), and some initialization quantities that are obtained from the output of the GNSS signal simulator. Once the configuration process is complete, data extracted from the reference trajectory is converted into a format appropriate to the INS simulator, and the inertial data simulation is performed. At this stage, data from both the GNSS side and INS side can be converted into a format suitable for use by the integrated INS/GNSS system (see Figure 7).
Figure 7. Data flow, Step 3. (Image: Authors)
EXPERIMENTAL WORK
Using the complete simulation system, several simulation data sets are used to verify the performance of the proposed algorithm in semi real-life scenarios. Each time a chosen scenario is run on the Spirent GNSS simulator, the software data is applied to the Spirent hardware to generate the RF signal, which is then applied to the input of the front-end unit to provide the corresponding I and Q signal streams. Meanwhile, the trajectory data is logged from the simulator to be used as a reference and then fed to the INS simulator to generate the corresponding raw IMU data. Finally, the I and Q and raw IMU data are combined (when the ultra-tight solution is used) in a software receiver code to extract the ultimate positioning solution. For scalar and vector-based signal tracking, only GPS data is used. One sample trajectory that simulates a land vehicle driving at low speed is selected to show results of the proposed method.
Table 1 shows initialization of the key parameters during the simulation period. A GPS-only satellite constellation is used. We also limited the maximum number of simulated satellites to seven.
RESULTS
The reference solution used to evaluate the proposed method and combined simulation system is the pure data sets extracted from the Spirent GNSS simulator. The figures below show results of 80 seconds of data processing. At around seven seconds of the period, a 43-dB signal drop was applied for 8 seconds on channel number 1, which is assigned to track PRN number 06. A similar signal drop is partially overlapped with this, but was applied for only 5 seconds on channel number 3, which is dedicated to track PRN number 21. The following abbreviations are used in the figures: ST for scalar tracking, VT for vector tracking, and UT for ultra-tight GPS/INS integration system.
Figure 8 and Figure 9 show the carrier frequency for PRN 06 and PRN 21. Large frequency errors (greater than 100 Hz) are noticeable in the scalar tracking system. The vector tracking system, however, was much less affected, showing more resistance to the drop in signal-to-noise ratio. The ultra-tight GPS/INS integration system was nearly unaffected and maintained a very accurate carrier frequency estimation throughout the simulated trajectory.
Figure 8. Estimated carrier frequency for PRN #6. (Image: Authors)Figure 9. Estimated carrier frequency for PRN #21. (Image: Authors)
The trend of the position errors is plotted in Figures 10, 11 and 12. The maximum position error was around 15 meters in the case of vector tracking, whereas the maximum position error from the ultra-tight system was below 4 meters in the worst case.
Figure 10. Position X error. (Image: Authors)Figure 11. Position Y error. (Image: Authors)Figure 12. Position Z error. (Image: Authors)
Velocity errors are depicted in Figures 13, 14 and 15. Velocity errors for the vector tracking system reached about 2 meters per second during the low signal-to-noise ratio period. However, they were only small fractions of a meter per second for the ultra-tight GPS/INS integration system.
Figure 13. Velocity X error. (Image: Authors)Figure 14. Velocity Y error. (Image: Authors)Figure 15. Velocity Z error. (Image: Authors)
CONCLUSIONS
This article shows the performance of a newly proposed ultra-tight GPS/INS integrated system using an RISS that is intended to enhance GPS receivers’ tracking ability in challenging environments, thus improving navigation availability. Additionally, we present a freshly combined GPS/INS trajectory data simulator that can be used to generate simulation data sets for all levels of GPS/INS integration. The two components of the simulator are demonstrated to be perfectly linked. Performance of the algorithm was tested using several trajectories, and the algorithm demonstrated durability against harsh signal degradation. Acceptable position and velocity errors were achieved. Expected future improvements to the algorithm aim to employ longer integration time, and the performance of different grades of IMUs are to be simulated.
ACKNOWLEDGMENT
This work described in this article was first presented at the ION GNSS+ 2018 conference in Miami, Florida.
MANUFACTURERS
The Spirent GSS6700 Satellite Navigation Simulation Signal Generator was used in these tests, with SimGen software. The NovAtel FireHose front-end generated the discretized data stream.
MALEK KARAIM is a Ph.D. candidate at the Department of Electrical and Computer Engineering, Queen’s University, Canada. He is working within the Navigation and Instrumentation Research (NavINST) Group at Queens’ University/Royal Military College of Canada.
MOHAMED YOUSSEF received his Ph.D. degree from the Department of Geomatics Engineering and the Department of Electrical and Computer Engineering, University of Calgary, Alberta, Canada. He leads GNSS R&D activities at Sony North America.
ABOELMAGD NOURELDIN is a cross-appointment associate professor at the departments of electrical and computer engineering in Queen’s University and the Royal Military College (RMC) of Canada. He is the director of the Navigation and Instrumentation Research Laboratory at RMC.
By Paolo Crosta, Paolo Zoccarato, Rafael Lucas and Gerarda De Pasquale, European Space Agency
Test set-up. (Image: Authors)
Most mass-market manufacturers have already developed a dual-frequency chip or will soon do so. What is still not completely clear is the main benefit of adding the second frequency. Is it beneficial just for correcting ionospheric error?
Will it provide an improvement of the ranging error thanks to the wideband nature of the signal broadcast on the second frequency and their multipath rejection capabilities? Is it improving the measurement quality by means of a higher transmitting power?
Could it be exploited as a source of data for the provision of accurate orbit and clock corrections? What is the best PVT algorithm to apply to a multi-constellation dual-frequency mass-market chip?
To answer these questions, an evaluation kit of the Broadcom chip BCM4775 has been tested — the first dual-frequency mass-market chip commercially available.
Results show:
the code noise (multipath) is often the main source of error, hiding the benefits of more accurate clocks and orbital data.
wide-band signals are very beneficial for multipath rejection. Position fix based on E5a-L5-only measurements even with fewer satellites can outperform L1-E1-only in tests performed this September (impact of the new Galileo satellites).
after deactivation of the duty-cycle tracking on Android phones, the carrier phase measurements are improved and we do not experience any longer filter resets in the position Kalman filter.
Unicore Communications has launched CLAP-B, a multi-GNSS/MEMS integrated inertial navigation board, which integrates a miniaturized high-performance inertial measurement unit (IMU) on a compact high performance GNSS board.
The high-accuracy GNSS positioning coupled with a high-precision gyro and accelerometer provides stable, continuous three-dimensional position, velocity and attitude, as well as original acceleration and angular velocity measurements, even in GNSS-denied environments, the company said.
CLAP (Concurrent Localization & Attitude Pilot) technology is a high-precision multi-sensor fusion positioning and orientation technology developed by Unicore.
The CLAP- B, along with all the UM and UB family of receivers, are on display at BDStar booth C12.0C.022 for the duration of Intergeo 2018 starting Oct. 16 in Frankfurt, Germany.
Key features of the CLAP-B include:
Compact size: 46 × 71 × 17.1 mm
5-ns RMS PPS output
0.1 degree (1σ) pitch and roll
WINS optimized technology (wheel INS) for vehicles, wheeled robotics
With the features of compact size, light weight, low power consumption, and easy for integration and mass production, CLAP-B is suitable for applications such as autonomous driving, smart surveying, unmanned aerial vehicles and various attitude stabilization platforms. Customer samples will be available by the first quarter of 2019.
NovAtel’s Sandy Kennedy offers an overview of the company’s SPAN CPT7 at ION GNSS+ 2018 in Miami. According to the company, the SPAN CPT7 is is a single enclosure GNSS and INS receiver powered by NovAtel’s OEM7 technology, which can deliver up to centimeter-level accuracy.
HERE Technologies and Altair Semiconductor are partnering to integrate HERE tracking and positioning software into Altair’s ALT1250 dual-mode LTE Cat-M1/NB-IoT chipset. This will enable HERE customers, system integrators and tracking device manufacturers to significantly reduce device time to market and provide hardware-based security.
The two companies plan to reduce overall power consumption of an end-to-end tracker application by optimizing the way the device communicates with the cloud.
The ALT1250 includes a GNSS receiver, an RF front-end supporting all commercial LTE bands within a single hardware design, a hardware-based security framework and an internal application MCU subsystem.
The integration of HERE tracking and positioning software means the chipset will be able to locate itself using the strength of cellular signals, even when it is not possible to obtain a GNSS fix.
The chipset can take advantage of the ability of HERE to provide online and offline positioning based on its database of cellular towers.
U-blox is collaborating with LynQ, which developed a location tracker that doesn’t use cellphones, networks, apps or monthly fees. By syncing up the devices before heading out, group members can find each other within a five-mile radius and link up again.
LynQ surpassed its fundraising target on the Indiegogo crowdfunding platform, raising more than $1.5 million, and is now on pre-sale.
The device uses the u-blox CAM-M8Q GNSS receiver, a GPS/GLONASS/BeiDou positioning module with an embedded antenna. With its slim size of 9.6 x 14 x 1.95 millimeters, it is easy to integrate it into handheld devices, u-blox said.
The first generation of the weather-proof tracker uses long-range, low-power radio frequencies to connect devices. Up to 12 people can join a group, split up, and use the only button on the clip-on device to toggle through the group members and find out in which direction and how far away each one is.
LynQ’s crowdfunding success shows the extent to which the company’s founders uncovered an unmet demand on the market. While smartphone-based solutions to locate friends and family, for instance in a crowd, abound, they are limited by the availability of mobile network reception.
LynQ’s location tracker helps friends regroup outdoors or at crowded events, parents keep an eye on their children, and caregivers watch over the safety of people with special needs, elderly family members, or loved ones suffering from diseases such as Alzheimer’s.
The tracker has been field-tested on numerous continents and in most topographies and use cases.
“We found the u blox CAM-M8Q to be the best solution for us to achieve the requirements our use cases demand,” said Drew Lauter, COO at LynQ. “We’re extremely price sensitive, yet we need a highly dependable GPS module; u-blox worked closely with us to deliver that.”
For u blox, accompanying LynQ in the development of its product has been an exciting adventure, said Suresh Ram, president of u-blox America. “We’re thrilled to see how well their idea has been received by the public and look forward to our continued collaboration in the future.”
Trimble has launched a new model of its premium GNSS receiver, the Trimble R10 Model 2 GNSS System. Designed to help surveyors in a wide range of industries work more effectively and productively, the Model 2 enables reliable, fast and accurate collection of survey data in the field, the company said.
Enhancements in Model 2:
The latest and most advanced custom Trimble survey GNSS ASIC with 672 GNSS channels for unrivaled GNSS constellation tracking, including GPS, GLONASS, BeiDou, Galileo, QZSS and IRNSS as well as the full range of SBAS. The Trimble R10 Model 2 tracks and processes all of today’s available GNSS signals and is designed to support planned GNSS signals and systems that may be launched in the future.
Improved reliability against sources of interference and spoofed signals.
Improved power management to increase battery life and operating time in the field on average by 33 percent.
Increased internal memory (6 GB) to store more than 10 years of raw observations.
Support for Android and iOS platforms to allow organizations with Bring Your Own Device (BYOD) environments to benefit from a premium survey GNSS receiver by using the mobile devices their field crews already have in their pockets.
The new features build on the Trimble R10’s core technologies, which include the Trimble HD-GNSS processing engine that enables points to be quickly measured with confidence, Trimble SurePoint technology for precise positioning capture and full tilt compensation, Trimble xFill technology for centimeter-level positioning during GNSS outages, and support for Trimble CenterPoint RTX corrections for RTK level precision worldwide.
Advanced GNSS rover system
The Trimble R10 Model 2 supports the recently released Trimble TSC7 controller and Trimble Access 2018 field software. The Trimble R10, in combination with the TSC7’s large 7-inch screen and faster processing power plus Trimble Access 2018’s new user interface and graphics capabilities, gives surveyors a superior, comprehensive solution for collecting and computing data in the field.
“These improvements ensure the Trimble R10 remains one of the most current and relevant GNSS survey solutions on the market today,” said Olivier Casabianca, director of global marketing for Trimble Geospatial. “By providing a powerful rover system such as the R10 Model 2, TSC7 controller and Access 2018 field software, Trimble continues its legacy of unmatched GNSS expertise and knowledge to advance the capabilities of surveyors around the world.”
GNSS receiver manufacturer Septentrio has added the AsteRx-i S to its GNSS/INS product portfolio.
The AsteRx-i S combines Septentrio’s compact, multi-frequency multi-constellation GNSS engine with an ultralight external industrial-grade MEMS-based inertial measurement unit (IMU).
Designed around demanding requirements for size, weight, power consumption and temperature variation, the AsteRx-i S is designed for various applications such as inspections with UAVs, UAS photogrammetry, automation, robotics and logistics.
Calibrated for wide temperature ranges, the AsteRx-i S delivers accurate and reliable GNSS/IMU integrated positioning to the centimeter-level, as well as full attitude at high update rates and low latency, the company said.
Key benefits for users include:
GNSS/INS positioning with 3D attitude: heading pitch and roll
AIM+ interference monitoring and mitigation system
High-update rate, low-latency positioning and attitude
Small and ultralight IMU (10 grams)
Robust calibration for wide temperature ranges
“We are delighted to broaden our AsteRx-i GNSS/INS solutions range, bringing maximum flexibility and choice to our customers,” said Francesca Clemente, product manager at Septentrio. “Whether for direct georeferencing in mapping applications with UAVs, for managing containers in a port or for innovative small robots in agriculture, the compactness, affordability and robustness of the AsteRx-i range allows our customers to focus on their success.”
