The ESA-funded SSRoverDAB+ project delivered its final results in June 2023.

Speaking from his company’s headquarters outside Berlin, Alberding GmbH Owner Jürgen Alberding explained the project’s rationale: “Our aim is to increase the availability of high-accuracy GNSS corrections in rural regions using DAB+ [digital audio broadcasting] transmission. This means overcoming computational and bi-directional communication limitations of network RTK as well as computing and comparing different SSR [state space representation]-based GNSS positioning solutions.”

Modern applications in precision agriculture and in the automotive and other industries all need continuous, highly accurate GNSS position information in real time. GNSS correction data required for this is typically transmitted to users via mobile internet. Due to dead spots, the corrections are often not available to users over a wide area.

“The growing demand for precise real-time corrections puts an increasing computational and bi-directional communication burden on network RTK service providers,” Alberding said. “The provision of GNSS corrections to an unlimited number of users without significant investments into the service infrastructure therefore requires a transition to a unidirectional broadcasting approach.”

Going about it in a new way

Along with project partners Fraunhofer, Geo++ and inPosition, Alberding set out to generate a broadcast-capable PPP-RTK correction data stream in an open data format with optimized bandwidth based on an existing GNSS reference station network. They established a reliable DAB+ data transmission channel and developed and adapted precise real-time PPP-RTK positioning and sensor fusion algorithms. The Alberding A10-RTK sensor served as a development and demonstration platform for extensive testing of the overall solution.

“The A10-DAB prototype sensors have been successfully used in practical field tests,” Alberding said, “and we got a very clear impression of the complexity of future interoperability tests for the SSR data format standardization.” Also speaking at the project final presentation was Fraunhofer’s Christian Fiermann, who said, “The developed hardware works as expected. The overall performance of the system is comparable to state-of-the-art automotive solutions. Decoding is possible even under weak signal conditions, and we were able to reactivate application type for SSR data in the DAB+ standard.”

“We now want to continue this work, to develop a DAB+ receiver module in a smaller form factor,” Alberding said, “and we want to add more processing power, to allow us to run the DAB+ decoding and processing in parallel.”Next steps for the consortium include production of second generation hardware prototypes in higher volume, to expand testing to a larger number of participants, as well as development of a highly integrated board that can be produced in numbers and sold to end users and system integrators.

SSRoverDAB+ is funded under the ESA NAVISP program, aimed at supporting the development of innovative competitive products in satellite navigation and other areas of positioning, navigation and timing.

The post SSRoverDAB+ Demonstrates New GNSS Corrections Approach appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Identifying seismic signals in GNSS reference stations using machine learning.

TIM DITTMANN, UNIVERSITY OF COLORADO, BOULDER/EARTHSCOPE

JADE MORTON, UNIVERSITY OF COLORADO, BOULDER

Continuous GNSS reference stations represent stable benchmarks for unsung but critical roles in the broader infrastructure: defining reference frames and providing relative corrections, to name a few. But, what if the stable reference station is shaking? A large earthquake will release sufficient energy to permanently deform the earth and vibrate its crust and a coupled GNSS reference antenna¹. Relatively weaker seismic signals at or below the perceived GNSS noise floor still can be problematic for reference products. However, these GNSS seismic ground motions identified amongst GNSS ambient noise are valuable records for seismic monitoring and research. 

In this article, we provide some of our motivation with respect to the scientific utility of ground motion observations, the benefits of using GNSS as a source of these measurements, and the current role of GNSS in seismic monitoring. We then present our work selecting an optimal processing method to pair with a machine learning algorithm. This approach builds on existing stand-alone GNSS seismic awareness to enhance GNSS’ contribution to seismic hazard operations.

Informed Infrastructure Seismic Preparation

Data-driven research allows seismologists to use extensive data archives to account for the complexity of geophysical sources and signal paths of past, present and future events. Catalogs of historical earthquake ground motion data inform models of rupture and energy propagation for informed infrastructure seismic preparation. Real-time ground motion data enable a class of warning called earthquake early warning (EEW) [1]. A successful EEW system detects the earthquake and decides its extent AFTER the earthquake rupture to then alert a population BEFORE peak ground shaking travels to a populated area. This provides those in the impacted area maybe tens of seconds warning to take life-saving actions, whether that’s to duck and cover or to stop a train or medical procedure. Finally, near real-time ground motion data informs maps of shaking intensity for targeted post-earthquake response, and are appended to the historical catalogs as the newest data point for improved preparedness.

These ground motion measurements are the lens into the earthquake system and are traditionally sourced from dedicated, long-standing inertial seismic monitoring infrastructure. The use of higher rate GNSS for seismology, or GNSS seismology [2], was born out of the precision achieved through the seminal engineering of GPS/GNSS and progressed over the last two decades of GNSS seismic research.

Two reasons for including GNSS as a source of seismic observations emphasized in our analysis are:

Increased spatial availability: Existing inertial and geodetic networks were largely built and continue to operate independently. Inclusion of both sensor types increases the density of ground motion observations. Such a densification is particularly valuable in relatively sparser regions [3], such as Alaska, but also adds redundancy and resilience to all existing overlapped networks. 

Dynamic range: Inertial instruments are engineered with specific signal spectral characteristics of interest. As a result, inertial instruments are orders of magnitude more sensitive to weaker signals, including p-waves, the earliest smaller amplitude waves of earthquakes, and surface waves from events halfway around the globe. However, seismologists identified that in the nearfield of the largest events (M7.0+) that information encoded in the slower, longer period, large amplitude displacement signals is required to differentiate the magnitudes of these largest events. Traditional inertial sensors struggle to capture this information due to instrumental reasons. GNSS, with no geophysical upper bound, readily provides either direct or single integration large displacement or velocity measurements necessary for this magnitude differentiation at frequencies down to their permanent offsets. 

One important distinction: In this article, we discuss GNSS seismology and present the complementary nature of inertial and geodetic sensors as stand-alone instruments, as this is currently the primary global infrastructure status quo. However, another closely related area of development and promise is seismogeodesy, or tight local integration of these sensors into a single measurement [4].

The USGS ShakeAlert, the operational EEW system in the United States, ingests GNSS data from the western United States geodetic reference networks through a multi-agency and university collaboration to complement inertial data ingestion. Residents of the western U.S. will benefit from this current culmination of nearly two decades of multi-national GNSS seismology research and engineering. GNSS displacements have been included in USGS post-process fault models [5], but not yet included in shaking intensity products or operational ground motion models.

The potential measurement range of GNSS seismology has not yet been realized operationally in part because of inherent GNSS noise characteristics. GNSS ambient position noise is predominantly the aggregate of timing effects of GNSS radio signal propagation through the atmosphere, satellite and receiver oscillators and the antenna radio frequency environment. Each are location- and time-varying influences, and distinct from the zero-baseline inertial sensor noise seismologists are most accustomed to. Current methods for discriminating signal from noise adopt variations on low-pass filters or static or dynamic thresholds from seismology. To gain the desired sensitivity to signals, high-levels of false alerts from these methods are mitigated through correlating with the inertial system as well as additional GNSS locations. This mitigation adds points of failure and latency and reduces the overall range of valid measurements included. The performance opportunity for improved seismic monitoring and EEW is to rapidly include additional, potentially spatially diverse and unsaturated, ground motion signals from GNSS sources with minimal delay by accommodating the higher dimensionality of GNSS noise.

Comparing Geodetic Processing Methods

To address the presence of seismic signals in GNSS data, we began with an evaluation of two geodetic processing algorithms [6]. Currently, most operational systems and research approaches ingest one of the various methods of precise point positioning (PPP-AR) to make continuous estimates of antenna positions in a global reference frame. These PPP algorithms accomplish this precision at approximately sub-centimeter level using sophisticated error corrections models from multiple sources to estimate carrier phase ambiguities. GNSS seismology requires only relative topocentric motion; consequently, PPP absolute estimates are flattened to relative east, north and up components from a reference position. Time differenced carrier phase processing (TDCP) is a lightweight processing technique first applied to seismic applications by [7]. TDCP single differences epoch-wise carrier phase measurements remove correlated error sources (e.g. troposphere). After removing the satellite velocity, a broadcast ephemeris is acceptable for this, and a least squares system of equations of all observed satellites resolves a topocentric antenna velocity vector and clock drift estimate.

We compared the relative signal to noise of PPP to TDCP to determine our processing method. For our noise estimates, we assembled a dataset of event-free 1 Hz GNSS observational data tracked by multiple receiver types, using a variety of antennas in diverse RF environments, across a hemispheric scale to account for a wide range of noise sources (Figure 3). For PPP processing, we used the UNAVCO/EarthScope PPP solutions from the Trimble RTX software [8]. For TDCP processing, we used the open-source python package SNIVEL [9], which uses GPS only, broadcast ephemeris and the narrow-lane L1/L2 carrier phase combination. From the event-free processed time-series, we estimated a stochastic noise for each station-processing method pair without cleaning or filtering the data. We used these thresholds to establish a statistical noise threshold distribution across this network wide dataset to represent the ambient noise distributions.

For our reference signals, we used empirical scaling laws that relate peak dynamics, earthquake magnitude and radius from the hypocenter. These scaling laws [10, 11] are derived from existing earthquake catalogs and useful for rapid magnitude estimation; the PPP-derived peak ground displacement (PGD) scaling law is part of the current ShakeAlert geodetic contribution. We estimated a signal-to-noise (SNR) metric using PGD or peak ground velocity (PGV) derived from respective scaling laws as our signal reference related to respective ambient noise levels. This SNR metric was estimated over a range of radii from a range of earthquake magnitudes. We found TDCP is more likely than PPP to detect the intermediate magnitude earthquakes (Figure 4) and has additional benefits of being a computational light-weight, open source processing method that doesn’t require external corrections for ephemeris or error source models.

Identifying Seismic Events in GNSS Timeseries with Machine Learning

The results of our ambient processing method comparison indicated TDCP offered lightweight geodetic processing with increased sensitivity, yet still demonstrated unacceptable operational false alarm rates from our statistical threshold. The complexity of GNSS noise coupled with the variability in seismic signals encouraged us to look to an alternative detection approach. Machine learning (ML) is now an ubiquitous tool in data science. Earth scientists have leveraged algorithms developed for natural language processing or image classification and applied them to a range of challenging problems [12] difficult to represent in physics-based models.

We set up a data-driven ML pipeline to train, validate and test a binary classification machine learning model [13]. The foundation of our data-driven experiment is a catalog of 1,706 5Hz TDCP velocity waveforms processed from the UNAVCO/EarthScope geodetic archive concurrent with 80 earthquakes ranging from magnitude of 4.9 to 8.2. An event-free 5Hz TDCP dataset from 30 minute windows prior to the events was included to ensure sufficient noise samples in training and testing for model generalization of these imbalanced datasets. We used 5Hz data to boost signal energy and reduce the likelihood of aliasing, and set a radius of sensitivity for each event as a function of magnitude given our previous sensitivity analysis.

Feature engineering in ML is the process of applying relevant domain knowledge to the ML model for successful classification. We evaluated several feature engineering strategies: the most effective strategy consisted of a combination of time- and lower frequency-domain features (1-30s period) extracted from overlapping 30 second windows. The three topocentric components’ features were labeled through visual inspection and concatenated into a single binary sample and label for each timestamp.

We chose a random forest classifier as our ML algorithm and adopted a nested cross validation technique in our classification training and testing  (Figure 5). This validation strategy allowed us to make training/testing splits of our data on the 80 discrete earthquakes, and evaluate our model’s performance on unseen events in training. We optimized the model on a balance of sensitivity scores and false positives using its F-1 score. A traditional accuracy metric on highly imbalanced classification data, such as our earthquake catalog, is typically not descriptive of performance (e.g., for events happening <1% of the time, you can miss 100% of the events and still be >99% accurate).

The random forest classifier achieved a 90% true positive rate of the station-event pairs (Figure 6) across the entire catalog. The stand-alone classifier substantially outperformed the existing threshold and filtering (e.g. short term average over long term average, STA/LTA) detection methods as shown in Figure 7. These performance results from the classifier’s combination of time- and frequency-domain features into its decision criteria could readily improve GNSS contribution to operational seismic monitoring and ground motion catalogs. Additional investigations in deeper learning models will likely enable researchers to ask more sophisticated questions.

Finally, we tested the timing of the classifier when run once per second on the 5Hz samples of test data not used in training. We found the classifier typically had its first detection approximately at or immediately after the anticipated seismic secondary wave arrival (Figure 8). This result explains our model, or alignment of our results with our domain knowledge that explains the model’s performance. The model did not detect the weaker seismic primary wave arrivals, but instead identified the larger, lower frequency ground motions of the seismic secondary and surface waves. This result also offers implications for GNSS and inertial complimentary hazard monitoring, particularly EEW when timing and accuracy are critical.

Conclusions

Ground motion observations are the data currency of earthquake hazard preparation, monitoring and research. Continuous high-rate GNSS reference stations offer an alternative source that expands the dynamic range of inertial-based ground motion measurements in the nearfield of the largest, most devastating earthquakes and spatially complements existing inertial infrastructure. Complex GNSS noise signatures have bounded operational incorporation of GNSS in these hazard systems. However, alternative, lightweight processing (TDCP) paired with machine learning (random forest classifier) offers enhanced confidence in signal from noise discrimination to confidently include these ground motion measurements in operational systems with minimal false alerting and without external corrections services. The global proliferation of higher-rate GNSS reference stations to support a variety of disparate position, navigation and timing applications could all become medium to large earthquake seismometers, alerting reference station users in addition to contributing to the global seismic monitoring systems. Furthermore, embedding TDCP processing coupled with ML at high rates (>=5Hz) at the network edge will enhance the next generation of geodetic sensor networks to stream higher rate velocities for seismic monitoring or archive denser raw observables for addressing future seismic research objectives. 

Acknowledgments

We would like to thank Yuinxang (Leo) Liu, Kathleen Hodgkinson, Brendan Crowell, David Mencin and Glen Mattioli. We acknowledge the open geodetic data available from the National Science Foundation GAGE facility operated by EarthScope and the open-source software used for GNSS velocity processing and their analysis, including GNSS velocity processing and machine learning libraries.

References

[1] R. Allen and D. Melgar, “Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs,” Annual Review of Earth and Planetary Sciences, 2019. 

[2] K. Larson, “GPS seismology,” Journal of Geodesy, 2008. 

[3] R. Grapenthin, M. West and J. Freymueller, “The Utility of GNSS for Earthquake Early Warning in Regions with Sparse Seismic Networks,” Bulletin of the Seismological Society of America, 2017.

[4] Goldberg, D. E., and Y. Bock (2017), Self-contained local broadband seismogeodetic early warning system: Detection and location, J. Geophys. Res. Solid Earth, 122, 3197–3220, doi:10.1002/2016JB013766.

[5] D. E. Goldberg, P. Koch, D. Melgar, S. Riquelme and W. L. Yeck, “Beyond the Teleseism: Introducing Regional Seismic and Geodetic Data into Routine USGS Finite‐Fault Modeling,” Seismological Society of America, 2022.

[6] T. Dittmann, K. Hodgkinson, J. Morton, D. Mencin and G. Mattioli, “Comparing Sensitivities of Geodetic Processing Methods for Rapid Earthquake Magnitude Estimation,” Seismological Research Letters, 2022.

[7] G. Colosimo, M. Crespi and A. Mazzoni, “Real‐time GPS seismology with a stand‐alone receiver: A preliminary feasibility demonstration,” Journal of Geophysical Research: Solid Earth, 2011.

[8] R. Leandro, H. Landau, M. Nitsschke and e. al., “RTX positioning: The next generation of cm-accurate real-time GNSS positioning,” Proceedings of the 24th international technical meeting of the satellite division of the Institute of Navigation, 2011.

[9] B. W. Crowell, “Near-field strong ground motions from GPS-derived velocities for 2020 Intermountain Western United States Earthquakes,” Seismological Research Letters, 2021.

[10] D. Melgar, B. Crowell, J. Geng, R. Allen and Y. Bock, “Earthquake magnitude calculation without saturation from the scaling of peak ground displacement,” Geophysical Research Letters, 2015.

[11] R. Fang, J. Zheng, J. Geng, Y. Shu and C. Shi, “Earthquake Magnitude Scaling Using Peak Ground Velocity Derived from High-Rate GNSS Observations,” Seismological Research Letters, 2020.

[12] K. Bergen, P. Johnson, M. V. de Hoop and G. Beroza, “Machine learning for data-driven discovery in solid Earth geoscience,” Science, 2019.

[13] T. Dittmann, Y. Liu, J. Morton and D. Mencin, “Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals,” Journal of Geophysical Research: Solid Earth, 2022.

Authors

Tim Dittmann is a data scientist at the EarthScope consortium and doctoral candidate at the Ann and H.J. Smead Aerospace Engineering Sciences at the University of Colorado, Boulder.

Y. Jade Morton is Helen and Hubert Croft Professor and Director of the Colorado Center for Astrodynamics Research in the Ann and H. J. Smead Aerospace Engineering Sciences Department at the University of Colorado Boulder. She received a Ph.D. in Electrical Engineering (EE) from Penn State. She is a member of the U.S. Space-based PNT Advisory Board, a recipient of the AGU SPARC award, the IEEE PLANS Kershner Award, and the Institute of Navigation’s (ION) Burka, Thurlow, Kepler, and distinguished service Awards. Dr. Morton is a Fellow of ION, RIN and the IEEE.

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In the wake of the latest COP session on international climate change, and as the world continues to deal with the effects of a warming planet, researchers say more and better data can help ameliorate the damage and provide answers on how to respond to it.

GIS giant Esri hosted a webinar on Nov. 30 to discuss how imagery from satellites and drones, combined with artificial intelligence and cloud storage, can help hold United Nations members and other states accountable in creating a more sustainable world.

The answer: By generating more data, taking more measurements, making the data more accessible and putting it into products that are easy to understand by even non-scientists.

Panelist Steve Brumby, cofounder and CEO of Impact Observatory, said he sees a pending era of “radical transparency” where people have the information they need to understand the choices their governments make.

“In the next 12 months, we’re moving from a situation where anyone can expect to get an annual map from one year ago … to having essentially continuous monitoring,” Brumby said. “Anybody in the world should be able to say, here’s my area of interest … let me understand how this area is changing.”

He said anyone should be able to get information on how much land has been urbanized, how much coastland has eroded, and other measurements. Such data should no longer be just the realm of experts, but “there should be no barrier to people getting that type of data.”

Panelist “Stinger” Gerald Guala, a program scientist at NASA, said a record number of Earth-observing satellites means more data than ever.

“We’ve got a lot of new data sets coming in,” he said. “More data is always better than less data. … we’ve got a bigger fleet [of satellites] right now that address climate change directly than we’ve ever had before. We’re going to have a lot more information in the future.”

Panelist Amos Desjardins, the data inspection, enhancement and delivery section chief at the U.S. Department of Agriculture, said his agency has about 7 petabytes of data, nine million frames of imagery, including one data set of a county in Wisconsin dating back to 1951.

“I always like to pivot and look backwards a little bit, and see where have we come from, what has changed over time?” he said. “Going forward, how can we collect imagery that will be useful?” The USDA maps the entire United States every other year (half one year, half the next) at resolutions as low as 15 centimeters, “and make that publicly available … so researchers and the general public can utilize that information.”

Brumby said the results from COP27, the 27th United Global Climate Change Conference, were disappointing to the conservation community in terms of practical results for fighting global warming.

With data from satellites, aircraft and on the ground, “things are changing faster than some of the models we hoped were correct not too long ago. The impacts on everyday life are already becoming undeniable, and there’s going to be a period where better decision making is not going to be some sort of luxury … it’s just going to be something people have to adapt to.”

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Hemisphere GNSS’s new Vega 34 OEM heading and positioning board enables users to upgrade to multi-frequency GNSS without changing pinouts. Integrators who use predecessor Hemisphere 34-pin products such as Crescent Vector H220 and Phantom 34 OEM boards can now transition to improved positioning performance and satellite tracking capabilities of the Vega series.

The product gives access to the company’s global reference station network and L-band satellite distribution supplying corrections for GPS, Galileo, GLONASS and BeiDou.

The Vega 34 board connectors have no circuitry changes and are identical for all Vector users who can now add Atlas H10 and H30 PPP in their solutions. “Vega 34 gives our integrators an easy path forward to enrich their own product offerings,” said Miles Ware, Director of Marketing at Hemisphere. “They can take advantage of other standard features like over 1100 tracking channels, Cygnus interference mitigation technology and spectral analysis.” 

The Vega 34 uses dual antenna ports to create a series of additional capabilities including fast, high-accuracy heading over short baselines, RTK positioning, onboard Atlas L band, RTK-enabled heave, low-power consumption, and precise timing.

Scalable Solutions

With the Vega 34, positioning is scalable and field upgradeable with all Hemisphere software and service options. Utilize the same centimeter-level accuracy in either single-frequency mode, or employ the full performance and fast RTK initialization times over long distances with multi-frequency multi-constellation GNSS signals. High-accuracy L-band positioning from meter to sub-decimeter levels available via Atlas correction service.

Key Features

• Extremely accurate heading with long baselines
• Available multi-frequency position, dual-frequency heading supporting GPS, GLONASS, BeiDou, Galileo, QZSS, IRNSS, and L band (Atlas®)
• Atlas L band capable to 4 cm RMS
• Athena GNSS engine providing best-in-class RTK performance
• Excellent coasting performance
• 5 cm RMS RTK-enabled heave accuracy
• Strong multipath mitigation and interference rejection
• New multi-axis gyro and tilt sensor for reliable coverage during short GNSS outages

The introduction of the Vega 34 board brings a new firmware release. Version 6.05 extends several features and improvements and introduces NavIC (IRNSS) tracking and positioning across the Vega and Phantom product lines. Both RTK and Atlas positioning solutions are enhanced with an improved performance in challenging environments. Users of the BeiDou satellite systems and B2b PPP integrators will see significant advances in their solutions.

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Applanix announced its Trimble AP+ Land GNSS-inertial OEM solution for accurate and robust position and orientation for georeferencing sensors and positioning vehicles in land mobile mapping applications. It enables users to accurately and efficiently track and monitor fleets, produce high-definition (HD) maps and 3D models, or act as a reference solution for advanced driver-assistance systems (ADAS) testing, even in challenging GNSS environments. 

The Trimble AP+ Land is a comprehensive solution for land vehicle applications that is small enough to easily integrate into the most compact mobile mapping systems. It is also compatible with virtually any type of mapping sensor, according to the company, including single or multi-LiDAR systems, video cameras, photogrammetric and panoramic cameras and other similar sensors.

Configurable to meet the mapping, positioning and direct georeferencing (DG) accuracy demands of mapping and positioning applications in challenging GNSS signal environments, the Trimble AP+ Land solution features:

• Applanix IN-Fusion+ GNSS-aided inertial firmware with Trimble ProPoint GNSS positioning technology
• Dual embedded survey-grade GNSS chipsets that can receive multi-frequency and multi-constellation signals
• Dual custom designed inertial measurement units (IMUs)
• Distance measurement indicator (DMI)
• Compact size
• Low power consumption
• Optional RTK and Trimble CenterPoint RTX real-time correction service support
• Full integration and post-sales support through the Applanix Global support network

“We have taken the most advanced features of Applanix inertial and Trimble GNSS technology, and packaged them into a powerful compact and versatile solution optimized for mobile mapping and positioning applications,” said Joe Hutton, Applanix’s director of inertial technology, air and land products.

The Trimble AP+ Land OEM solution is supported by the Applanix POSPac MMS post-processing software, which features Trimble CenterPoint RTX post-processing for centimeter-level positioning globally without the need for base stations. These capabilities enable integrators to produce an efficient land mobile mapping system.

For LiDAR integrators, the Trimble AP+ Land OEM is compatible with the POSPac MMS LiDAR QC tools.  SLAM technology computes the IMU to LiDAR boresight misalignment angles and also adjusts the trajectory to achieve the highest level of georeferencing accuracy in the generated point cloud.

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By Miguel Amor, Chief Marketing Officer
Hexagon’s Autonomy & Positioning division

I had my first interaction with a geodetic GPS receiver in the early ’90s. In my freshman year of college, a professor demonstrated how to use two single-frequency receivers to achieve centimeter accuracy for post-processing observations. I didn’t know at the time that I would spend the next 30 years deeply involved in and contributing to this innovative positioning technology.

At the time, it seemed revolutionary to me that we could obtain centimeter-level coordinates in a global reference framework without the labor-intensive traditional methods of geodetic and topographic observations. I remember how groundbreaking it was when I heard that it was mathematically and technically possible to make these observations in real-time in kinematic mode—and that this theory would be a reality very soon.

At that moment, the possibility of what would become the RTK technique was thrilling, and I began to imagine what future work would be like for my fellow students and me. Consciously, I was witnessing the beginning of corrections technology that would revolutionize the science of geodesy and land surveying. I couldn’t have imagined the impact of corrections 30 years later for all kinds of sciences and activities requiring such precise positioning.

A few years later, the first RTK equipment was established for users to achieve centimeter positioning in real-time. My earliest experience working with an RTK system was only comparable to the first call I received on my first mobile phone—curiously, also in the mid-late ’90s.

More or less simultaneously, I experienced the benefits of the newly developed PPP technology using geostationary satellites. PPP was an exciting technology too, although the precision was far from that necessary for surveying and geodetic work, and the convergence time was still too long to be productive in many applications.

Despite the advantages of these emerging RTK and PPP technologies, geodetic and topographic work often still required direct observations of geodetic monuments. This work was both laborious and logistically complex. My legs still ache when I recall the long days carrying heavy GPS equipment, hiking for miles along the crest of a mountain range, conducting GPS observations of geodetic monuments and moving UHF radios to different areas to stake out locations of future wind turbines.

For corrections technology to best serve geodesy, we needed to answer the following question: How do we achieve centimeter-level coordinates instantly anywhere in the world within a global geodetic reference framework without local communication infrastructure?

It seemed the answer could be found in combining the benefits of RTK and PPP technologies. This was my dream and the dream of many geodesists and surveyors; easy to dream, but not so easy to achieve!

Fast-forward 30 years. In January, Hexagon’s Autonomy & Positioning division announced improvements to the TerraStar-C PRO correction service that makes it possible to obtain 2.5 centimeters in less than three minutes anywhere in the world.

Between that early dream and today, GNSS technology has evolved, including developments across GPS L2C and L5, network RTK, Galileo, GLONASS, BeiDou and others. PPP technology is reaping the benefits of these innovations. By leveraging all new GNSS constellations and all frequencies available while overcoming significant and challenging new developments in the PPP algorithms in both the server and client, the dream has come true. We call it RTK From the Sky technology.

What would the person writing these lines have given during one of those mountain hikes to be able to obtain a point with 2.5 centimeter precision with a global reference framework in less than three minutes without external communications?

A lot!

I never would have imagined how the same GNSS technology now enabling instant centimeter positioning would also be a critical innovation for the development and future of our society. The reliability, productivity and safety of agricultural tractors, construction machinery, hydrographic vessels and many other autonomous vehicles across innumerable applications are now possible, thanks to that dream of geodesists and surveyors.

Billion-dollar investments in autonomous technologies, arguably one of the biggest revolutions in human history and the key to a sustainable future, will rely on this technology that originated from the dream of instant centimeter positioning anywhere in the world.

My congratulations and heartfelt thanks to my colleagues for making it possible for this dream to come true: RTK From the Sky!

(Top image: Wind turbine locations staked out by Miguel Amor in the mountains of Spain.)

The post Surveyors’ Dreams that Changed the World: A Personal Recollection appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Trimble has introduced the R750 GNSS modular receiver, a connected base station for use in civil construction, geospatial and agricultural applications. The R750 provides improved base station performance, giving contractors, surveyors and farmers reliable and precise positioning in the field.

The Trimble R750 utilizes a single Maxwell 7 ASIC with 336 GNSS tracking channels and offers all GNSS constellations plus the latest signals (such as GPS L1C and BeiDou-III). Featuring a built-in 4G LTE modem, Wi-Fi, Bluetooth and Ethernet, the Trimble R750 can broadcast RTK corrections to rovers via the internet, such as receiving corrections from an NTRIP base station or VRS network. It also easily connects to the receiver Web User Interface to configure settings and functions, like GNSS data logging.

The R750 can broadcast real-time kinematic (RTK) corrections for a wide range of applications, including seismic surveying, monitoring, civil construction, precision agriculture and more. Access to all available satellite signals provides improved performance and reliability when used with a Trimble ProPoint GNSS rover. ProPoint gives users improved performance in challenging GNSS conditions.

Key users in geospatial will include:

• Land surveyors in the areas of cadastral, topographic, construction, pipeline and mine, typically using the Trimble R750 as a base station.
• Geodetic surveyors, including national and state mapping agencies working on static observation campaigns for projects like geoid slope validation surveys and crustal movement monitoring, and Departments of Transportation (DOTs) doing control network surveys for road and rail.
• Seismic surveyors doing natural resource exploration via a vehicle-mounted rover system and often operating in remote and wooded locations.

Featuring a built-in LTE modem, the R750 can provide corrections via the internet, making it easier to extend the range of a base station anywhere with cellular coverage. The built-in modem also provides remote access and management, delivery of email alerts and notifications, and data transfer capabilities between the field and the office.

“The ability to manage the base station remotely and to receive status notifications about the unit while in the office reduces downtime and the need to travel to the site,” said Scott Crozier, vice president of Trimble Construction Field Solutions.

Combined with Trimble 4D Control real-time monitoring software, users can capture high-frequency 3D positions for alarming and reporting on movement. The R750 offers multiple communication methods that provide flexibility for customers on how they deploy their monitoring system.

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Trimble has rolled out the latest version of its core geospatial automated monitoring software, Trimble 4D Control version 6.3. The software provides automated movement detection to enable informed decisions about infrastructure for surveying, construction and monitoring professionals.

Version 6.3 adds new capabilities for the software to work in combination with the Trimble SX Series Scanning Total Stations’ advanced imaging and measurement capabilities. This version also supports vibration and weather-station sensors and a streamlined workflow between the Trimble Access Monitoring Module in the field with the new T4D Access Edition used in the office.

Enhancements to the geospatial monitoring software provide increased accuracy; simplified sensor data collection, reporting and alarms; and make it possible to seamlessly move from semi-automated to fully automated monitoring on a project.

Integrated with the SX Series Scanning Total Station, T4D brings VISION imaging technology and high-accuracy Lightning 3DM technology for more accurate measurements, enabling a more dense target placement on linear corridors such as rail tracks, tunnels, roads and bridges. A live video feed makes it possible to better understand site conditions, manage target placement remotely and capture images for use with T4D visual inspection capabilities. These images can be compared over time and viewed next to the displacement or movement charts. This enables users to identify the potential cause of displacement and record movement changes over time.

Vibration and Weather

With the upgrade, vibration sensors from Syscom allow surveying, civil and geotechnical engineers to easily combine geodetic and geotechnical information supporting high-frequency and event-based vibration information. This data is often used for mandatory reporting on civil and infrastructure projects.

Integration with the Vaisala weather station analyzes the impact of environmental conditions such as temperature, rainfall, wind and atmospheric pressure in combination with other geospatial and geotechnical monitoring information, which is useful for slope stability analysis in mining, landslide and dam monitoring operations.

From Semi- to Fully Automated Monitoring

Automated, seamless transfer of field data from the monitoring module to software in the office makes it possible to scale monitoring operations from a semi-automated to fully automated monitoring system while maintaining the continuity of historical data in the same charts and reports.

The post Sensor Integration and Support Expanded for Geospatial Monitoring appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The RINEX Working Group of the International GNSS Service (IGS) has released the new Receiver Independent Exchange Format Version 4.00 (RINEX4), as of December 1, 2021.

RINEX is is a data interchange format for raw satellite navigation system data. This allows the user to post-process the received data to produce a more accurate result — usually with other data unknown to the original receiver, such as better models of the atmospheric conditions at time of measurement. RINEX is the standard format that allows the management and disposal of the measures generated by a receiver, as well as their off-line processing by a multitude of applications, whatever the manufacturer of both the receiver and the computer application. (Wikipedia)

The IGS characterizes RINEX Version 4 as a necessary step to support the modern multiGNSS navigation messages by introducing and defining navigation ‘data records’ to hold both individual satellite navigation messages, constellation-wide parameters and global parameters as transmitted by the different GNSS constellations.

RINEX 4.00 is a major revision of the format document to modernize the Navigation message files to be able to accommodate the new navigation messages from all the GNSS constellations, and system data messages such as; ionospheric corrections, earth orientation parameters and system time offsets. The Observation file format remains the same with some added QZSS signals and tracking codes to fully support the upcoming L1 C/B signal. The Meteo file format also remains the same. All RINEX file types also have new optional header lines to support FAIR data usage; Finding, Accessible, Interoperable and Reusable data.

For more information, see the RINEX page on the IGS website. Image above courtesy IGS.

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In this webinar, Adam and Ashley will deep-dive into LiDAR surveying and explain how Cordel leveraged innovative technology to push the boundaries of the industry. They will cover the challenges in UAV-based surveying, in balancing cost and altitude restrictions, with LiDAR point density and INS accuracy to achieve the best overall solution.

Land-based surveying will also be discussed, where GNSS denied environments introduce particular challenges in the overall solution.

They will also talk about the current evolution of the tech stack and what to expect in the future.

If you’re not able to make the session time, a recording of the webinar will be available for all registrants.

To register: https://lu.ma/xd5ag3ow

About Advanced Navigation: Advanced Navigation is a worldwide leader in AI-based navigation solutions and robotics. The company develops solutions from the ground up with a long-standing history of building bespoke hardware and software for our customers.

About Cordel: NextCore is the leading developer of UAV LiDAR payload systems across the world. The NextCore company is part of the Cordel Group which improves the inspection and visualisation of assets through the utilisation of hardware and software technology including LiDAR and Artificial Intelligence.

The post INS Technology Advances LiDAR Surveying Capabilities appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.