A Fregat booster successfully delivered a Glonass-K navigational satellite into its designated orbit, Russia’s Defense Ministry reported on Oct. 10. Glonass-K No. 17L is the fifth K satellite to join the constellation.
“A Soyuz-2.1b medium-class carrier rocket that blasted off at 05:52 a.m. Moscow time on October 10 from the Plesetsk spaceport in the Arkhangelsk Region successfully delivered a Russian Glonass-K navigational satellite into the target orbit at the designated time,” the ministry said in a statement.
Liftoff and the delivery into the designated orbit proceeded in normal mode, the ministry said, and the ground-based facilities of Russia’s Aerospace Forces assumed control.
“The Russian Glonass-K navigational space vehicle launched on Monday, October 10, from the Plesetsk spaceport by a combat team of the Space Troops of the Aerospace Forces was delivered into the target orbit at the designated time and placed under the control of the ground-based facilities of the Titov Main Testing Space Center of the Aerospace Forces’ Space Troops,” the statement said.
Stable telemetry communications have been established and are being maintained with the satellite. The space vehicle’s onboard systems are operating in normal mode, the ministry said.
The Glonass-K is a third-generation satellite of the Russian global navigation satellite system (Glonass). The satellite was engineered and manufactured by the Reshetnev Information Satellite Systems Company (part of Russia’s State Space Corporation Roscosmos). The satellite was developed to replace the Glonass-M family of space vehicles.
Military officials from across all branches, federal security personnel, and industry leaders gathered at the AUVSI Defense conference, held Sept. 22 in Alexandria, Virginia, to discuss critical issues surrounding the integration of uncrewed technologies.
In a publication released Oct. 11, AUVSI Senior Economic Research Analyst Aaron Bull summarized key topics discussed at the event, including:
defense priorities for the next-generation uncrewed system
how uncrewed systems will impact the ways wars are fought
lessons learned by senior defense leaders from the Bayraktar TB2 in Ukraine.
The Bayraktar TB2 surveillance/attack drone (Photo: Baykartech)
Highlights from the Report
Flexibility in the fighting force is needed, which affects the defense requirements for autonomous vehicles heading to the battlefield.
Multiple speakers pointed to the Turkish Bayraktar TB2, a cost-effective combat-capable drone purchased and fielded by the Ukrainian armed forces that has been a game changer for Ukraine since the war began. While the drone is not top of the line, it was fielded quickly, required little training and could be fitted for a variety of purposes. As a result, “Nearly every speaker came prepared to discuss the need for developing multiple layers of flexibility around the U.S. fighting force,” Bull writes.
An uncrewed vehicle that can be refitted for multiple missions of different types offers an inherent advantage for missions, and it requires supporting logistical infrastructure.
Requirements include:
flexibility and disguise of role
ability to outfit to different technical and operating capabilities
flexibility to operate with different levels of human interaction
SBG Systems has launched the Quanta Micro, a navigation system in an extremely compact form factor with a dual-frequency, quad-constellation GNSS receiver for centimeter position with a high-performance inertial measurement unit (IMU).
The Quanta Micro is a real-time kinematic (RTK) capable, miniature inertial sensor that measures 50 mm x 37 mm x 23 mm and weighs 38 g. Its high-end performance includes centimeter positioning, roll/pitch with less than 0.02° error, and heading with less than 0.06° error. It is suitable for all applications, especially those that have low size, weight, power and cost (SWaP-C) requirements. Quanta Micro has already been selected for the development of lidar payloads for UAV and mobile-mapping systems.
To achieve such performance in the harshest conditions, Quanta Micro benefits from SBG Systems’ unique experience in designing and manufacturing inertial sensors, including an individual calibration of each of the manufactured sensors across the full range of working temperatures (–40° C to +85° C).
Lidar point cloud of SBG Systems’ head office created using the Quanta Micro. (Photo: SBG Systems)
Despite its compact form factor, Quanta micro embeds all the features usually present in the other SBG inertial sensors: a built-in datalogger, Ethernet connectivity, a PTP server, multiple serial ports, a CAN port and more. It is easy to configure with a user-friendly built-in web interface; but can also be configured using SBG systems’ API or ROS drivers.
While the Quanta Micro supports dual GNSS antenna mode to improve heading accuracy in low dynamic applications, it has been designed to maintain exceptional heading performances even in a single antenna configuration. This makes it the right tool for UAV payloads that cannot embed two GNSS antennas.
Post processing with Qinertia. To further enhance its extreme real-time performances, the data acquired from the Quanta Micro can easily be post-processed using Qinertia: SBG’s own post-processing kinematic tool. This allows users to process the data with tight coupling of the GNSS and inertial data, and a merge of forward and backward solutions allowing it maintain centimeter precision even during multiple seconds of GNSS outages. It also improves heading errors to less than 0.035° and roll/pitch to less than 0.015°.
Hexagon AB, which offers digital-reality solutions combining sensor, software and autonomous technologies, will integrate AVVIR’s artificial-intelligence-powered technology stack into its portfolio of solutions that address challenges of the construction lifecycle.
Since 2017, AVVIR has enabled intelligent, data-driven job sites that empower commercial, infrastructure and industrial construction professionals to reliably and safely deliver on schedule and within budget, Hexagon stated in a press release.
AVVIR’s reality-analysis platform is focused on building information modeling (BIM). It is designed to improve project workflows, schedules and outcomes by leveraging onsite reality-capture data, enriched BIM models and artificial intelligence. The solution gives construction teams control with automated schedule tracking, cost and earned value analysis, installation issue detection, and an updated BIM with as-built conditions.
The world watched in awe and a bit of terror as the 23-inch polished metal sphere arced across the sky. Its elliptical, 65-degree declination low-Earth orbit covered virtually the entire planet. Its beep, beep, beeping could be easily heard by professional and armature radio operators alike.
The Soviet Union had just put the world’s first artificial satellite, Sputnik I, in space. It was Friday, the 4th of October, 1957.
The Soviets had gained the high ground, quite literally. The Space Race had begun. As had the West’s greatly increased focus on education in science and engineering. School children in the United States would never lack for homework again.
On Monday, the 7th of October, scientists William Guier and George Weiffenbach arrived to work at the Johns Hopkins University Applied Physics Laboratory in Laurel, Maryland. To their surprise, they found no one had been listening in on Sputnik’s signal over the weekend. So, they decided to do just that. Just for good measure, they also recorded it.
Thus began a series of events that led directly to every satellite navigation system that has come since.
Guier and Weiffenbach’s story was documented in the Johns Hopkins Technical Digest in 1997. It is a fascinating tale of discovery. I highly recommend it.
And it may be a good reason for October 7 to become “World SatNav Day.”
The development kits will bring together u-blox’s centimeter-level positioning and wireless communications expertise and services to support faster time-to-market for new products
The XPLR-HPG-1 high-precision GNSS explorer kit. (Photo: u-blox)
U-blox has announced new explorer kits to make it quicker and easier for engineers to design and evaluate products requiring centimeter-level positioning capabilities.
Set to launch in early 2023, the ready-to-use XPLR-HPG-1 and XPLR-HPG-2 solutions will combine u-blox’s unique offering across the key technologies required to achieve highly precise positioning.
As well as an open microcontroller unit (MCU), the kits will include high-precision GNSS positioning with real-time kinematic (RTK), dead-reckoning, cellular, Wi-Fi and Bluetooth communications, along with the necessary antennas.
The kits are designed to integrate seamlessly with complementary u-blox services, such as PointPerfect GNSS augmentation service and the ubxlib software component.
The XPLR-HPG-2 High precision GNSS explorer kit. (Photo: u-blox)
The kits will assist engineers working in areas such as micro-mobility and low-speed robotics, helping them build, test and demonstrate early-stage proofs of concept more quickly, supporting faster overall time-to-market.
Both explorer kits will include the full gamut of u-blox technology and software required.
The modular XPLR-HPG-1 kit will be based around the wireless MCU in the u-blox NORA-W106 , with its Wi-Fi and Bluetooth LE capabilities, and will give engineers flexibility to adjust their solutions to their precise needs, using MIKROE Click boards featuring a variety of u-blox modules. The kit will include three Click boards, which respectively incorporate the ZED-F9R high-precision RTK GNSS module, the LARA-R6001D LTE Cat 1 module (global coverage and with built-in MQTT client), and the NEO-D9S L-band correction data receiver module. Engineers can purchase others based on their application’s needs. The kit’s source code will include example software for the Espressif IoT Development Framework (ESP-IDF), based on ubxlib software components.
The compact XPLR-HPG-2 will deliver an integrated solution, incorporating the ZED-F9R high-precision RTK GNSS, LARA-R6001D LTE Cat 1 (with global coverage and built-in MQTT client) and NEO-D9S L-band correction data receiver modules, as well as the NINA-W106 with its MCU, Bluetooth LE and Wi-Fi capabilities.
The Ariane 6 launch vehicle program has taken a dramatic step towards first flight with the start on Oct. 5 of hot-fire tests of the rocket’s upper stage and its all-new Vinci engine, according to the European Space Agency (ESA).
The tests are a significant step forward. They are being conducted using the specially built P5.2 test bench for engine and stage testing at the German Aerospace Center (DLR) in Lampoldshausen. The P5.2 test bench subjects the entire upper stage to operating conditions representative of a flight from Europe’s Spaceport in French Guiana, with the exception of vacuum and microgravity.
New Vinci Engine
Vinci, the upper stage engine of Ariane 6 fed by liquid hydrogen and oxygen, can be stopped and restarted multiple times — a critical capability for the complex missions demanded by launch customers today.
The rocket can place several satellites into different orbits and de-orbit the upper stage, leaving a minimum of hazardous debris in space. Vinci also has been developed for reliability, simplicity and lower costs.
Replacement Heavy Launcher
This test series is a critical milestone on a development path that will soon see Ariane 6 replace Ariane 5 as ESA’s heavy launcher.
For more than a quarter century, Ariane 5 has been a reliable partner for commercial, institutional and scientific clients. One of its most notable missions was the Dec. 25, 2021, flight that carried the NASA/ESA/CSA James Webb Space Telescope to its operational outpost in deep space.
But Ariane 6 will be an even more versatile vehicle, strengthening Europe’s autonomy in accessing space.
Auxiliary Power Unit
The tests being run at Lampoldshausen are also evaluating an innovative auxiliary power unit (APU) that works in tandem with the Vinci engine and is instrumental to Ariane 6 upper-stage performance.
To restart in space, earlier engines relied on large quantities of tanked helium to generate the necessary pressure and temperature in the propellant tanks and to ensure there are no bubbles in the fuel lines. However, the APU delivers these conditions using only small amounts of the cryogenic hydrogen and oxygen already carried in the main tanks.
Heading to ESTEC
The test series is being run by DLR and ArianeGroup, the Ariane 6 launcher prime contractor. When the test series is complete, the upper stage — integrated by ArianeGroup at its facility in Bremen, Germany — will be shipped to ESA’s ESTEC technical center in the Netherlands for stage separation and acoustic tests.
Ultimately, the Lampoldshausen tests will investigate hardware behavior and system function of the complete stage with its tanks, engines and avionics.
“The preparation for these hot firing tests is even more complex than for an actual launch,” said Ariane 6 launcher program manager Guy Pilchen. “Our colleagues in Lampoldshausen have decades of experience in rocket propulsion with extremely advanced test facilities. With ArianeGroup colleagues to control the upper stage and DLR people operating the test bench, we couldn’t ask for a better team.”
Space independence for Europe
ESA Director of Space Transportation Daniel Neuenschwander said that this new engine and the upper stage it powers are indispensable components of Ariane 6 and its objective — to guarantee that Europe maintains independent, competitive and sustainable access to space.
“It’s a fact in the 21st century that Europeans depend on space for safety, prosperity and security,” Neuenschwander said. “Europe needs to work toward complete autonomy in accessing and operating in space. Ariane 6 is key to this, and we are eager to see the liftoff from Europe’s Spaceport in French Guiana.”
Ariane 6 Vinci engine testing at DLR Lampoldshausen. (Photo: ESA)
Global corporation VIAVI Solutions Inc. has completed the acquisition of Jackson Labs Technologies, a leader in positioning, navigation and timing (PNT) solutions for critical infrastructure serving both military and civilian applications.
Jackson Labs develops and supplies modules, subsystems and box-level solutions that include front-end receivers, transcoders, rack-mounted equipment, and patented retrofit technology. Their broad customer base includes armed forces, defense contractors, energy distribution infrastructure, low-Earth-orbit (LEO) operators and 5G service providers.
Jackson Labs’ next-generation M-code solutions complement and advance VIAVI’s timing and synchronization portfolio at a time when PNT requirements for defense, space, commercial aviation, transportation and telecommunication networks are expanding and becoming increasingly critical.
“As telecommunications, avionics and mission-critical infrastructure adopt next-generation technology, legacy timing and synchronization protocols are no longer sufficient. Jackson Labs is a trusted provider of PNT solutions in these markets, and we look forward to addressing these opportunities together,” said Oleg Khaykin, president and CEO of VIAVI. “With this acquisition, we are continuing to drive operational scale via the addition of advanced technology and high-performance products that address market segments with strong growth and profitability.”
“Being a part of VIAVI will significantly expand Jackson Labs Technologies’ market reach worldwide, and allow us to further deliver world-class solutions for the rapidly developing PNT landscape as it enters a new era,” said Said Jackson, CEO of Jackson Labs Technologies.
DelMorgan & Co. acted as the exclusive financial advisor to Jackson Labs in connection with the transaction. Terms of the transaction are not being disclosed.
About VIAVI
VIAVI s a global provider of network test, monitoring and assurance solutions for communications service providers, enterprises, network equipment manufacturers, original equipment manufacturers, government and avionics. It helps customers harness the power of instruments, automation, intelligence and virtualization.
VIAVI is also a leader in light management solutions for the anti-counterfeiting, consumer electronics, industrial, government and automotive markets.
VIAVI operates offices throughout North, Central and South America, Europe, Africa, the Middle East, and the Asia-Pacific, including China and Japan.
The Indian government is pushing smartphone makers to sell devices that receive NavIC signals along with GPS.
India originally stated NavIC would be required in smartphones sold starting in January 2023, according to Reuters, but strong reaction from smartphone manufacturers Apple, Xiaomi and Samsung apparently caused the government to push back or remove the deadline.
A deadline of January 2023 would not allow enough time for smartphone makers to integrate NavIC-enabled receivers to their devices. Steps include redesign, securing parts, testing and assembly. Many smartphones sold in India by the companies are economy-level devices priced under US$200.
The three tech giants met with government officials, seeking an extended target date of 2025, Reuters reported.
However, India’s Ministry of Electronics & IT issued a statement via Twitter :
A media report has claimed citing a meeting that mobile cos were asked to make smartphones compatible with NavIC within months. This is to clarify: (1) No timeline has been fixed. (2) The cited meeting was consultative; and (3) the issue is under discussion with all stakeholders.
NavIC (Navigation with Indian Constellation) is the operational name for the Indian Regional Navigation Satellite System (IRNSS) developed by India’s space agency for military and commercial purposes. NavIC consists of eight satellites that cover the Indian mainland and the region extending up to 1,500 km from its boundaries.
“NavIC can help in navigation on land, air, sea and also in disaster management,” Science & Technology Minister Jitendra Singh said in a press release. “NavIC satellites are placed at a higher orbit than the GPS of United States. NavIC satellites are placed in geostationary orbit (GEO) and geosynchronous orbit (GSO) with an altitude of about 36,000 km; GPS satellites are placed in medium earth orbit (MEO) with an altitude of about 20,000 km.”
“NavIC uses dual-frequency bands, which improves accuracy of dual-frequency receivers by enabling them to correct atmospheric errors through simultaneous use of two frequencies,” Singh said. “It also helps in better reliability and availability because the signal from either frequency can serve the positioning requirement equally well.”
The Google Smartphone Decimeter Challenge (SDC) competition, co-sponsored by the Institute of Navigation (ION), took place this summer. For the competition, teams developed high-precision GNSS positioning using a pool of smartphone GNSS + inertial measurement unit (IMU) datasets accompanied by high-accuracy ground truth. Teams competed to achieve the best location accuracy with the datasets provided. Winners received cash prizes and sponsored attendance at the ION GNSS+ 2022 conference in Denver, Sept. 19-23, to present their results.
Origins
The SDC has its origins in the Android Operating System, which is an open-source platform. In 2016, Google made GNSS raw measurements available as a public application programming interface (API) on all Android phones. Since then, the available measurements have become more sophisticated and more accurate. For example, dual-frequency carrier-phase data is now available on many Android phones. This enables new areas of research.
Goals
The competition had two goals:
• Stimulate the research and development of high-accuracy algorithms that can produce submeter position accuracy on phones.
• Establish a publicly accessible repository of labeled data so that all future research on location algorithms can be judged in a consistent way against a standard set of data.
The first goal was met beyond our expectations. A total of 1,381 teams participated in the two competitions of 2021 and 2022. Discussion among competitors on the competition platform (kaggle.com) was wide-ranging, incredibly collegial, and beneficial to the entire community.
Competitors have written and shared detailed descriptions, and these have been reviewed and commented on by other competitors. Moreover, winners have written formally peer-reviewed papers and made presentations at the ION GNSS+ conferences, which are available from ion.org.
The second goal is a work-in-progress and is intended to be the legacy of the events.
Legacy
Disciplines such as machine learning have established benchmarks that make it possible to compare new approaches to previous ones in a proper quantitative way. In the GNSS community, this convention has been missing — a glance across papers at conferences will show that different algorithms tend to be presented with different test data and different metrics. Usually, the authors collect this data, and it is often fairly sparse (one or two drive tests, for example). Also, the reader never knows whether the data was cherry-picked (were bad results not mentioned?).
The SDC data provides:
• 206 different drive tests
•86 total hours of dual-frequency (L1, L5) data with code and carrier-phase measurements
•All labeled with ground-truth positions and velocities collected using NovAtel SPAN ISA-100C, with precise lever-arm compensation and validated with Google’s analysis tools.
The Kaggle site allowed users to submit their results, then automatically scored them against the ground-truth data. We advocate that all GNSS researchers use this resource to measure their location algorithm improvements in a standard way. This creates trust in published results, accelerating the recognition and adoption of truly great improvements for the benefit of the entire industry and GNSS users worldwide.
The top three winners of this year’s Smartphone Decimeter Challenge described their projects to Matteo Luccio, GPS World editor-in-chief.
Suzuki
Taro Suzuki, Chiba Institute of Technology
1st Place Winner: Two-Step Optimization of Velocity and Position using Smartphone’s Carrier Phase Observations
What is your research focus and how does it relate to the contest?
My current research focuses on the accurate positioning of vehicles and mobile robots in urban environments where GNSS multipath occurs. I usually use commercial GNSS receivers for my research. This competition is very relevant to my current research, except that the smartphone is replacing a receiver.
How long have you been developing the technology or approach you used to win the contest?
The competition was held for three months, but I concentrated my efforts on the past three weeks. However, I used technologies and resources developed in my previous research (for example, source code developed in last year’s competition).
Have you participated in previous editions of this contest?
Yes, I participated in the last competition and won. The approach used in this year’s competition is based on the method used to win last year’s competition, with additional innovations and improvements.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The competition provides a training dataset, which contains raw GNSS observations from a smartphone installed on a vehicle as it travels on real roads. In addition to GNSS observations, the training dataset contains the ground truth of the smartphone’s position. The training dataset includes a wide range of GNSS signal conditions, such as driving on highways around San Francisco and Los Angeles, driving on tree-lined urban streets, and driving in tunnels and under overpasses. I have developed an algorithm that uses a training dataset containing ground truth to accurately estimate the location of smartphones in a variety of GNSS signal reception environments.
What accuracies were you able to obtain?
The competition metric was “average of 50th and 95th percentile horizontal errors.” The metrics are computed for each of the 36 runs in the test dataset, which are divided into public and private groups, then the metrics are averaged in each group to compute the final score. My final score was 1.382 m for public and 1.229 m for private. The best score given after the competition was 1.372 m for public and 1.197 m for private. The final result achieved sub-meter accuracy in the median (50th percentile).
What are the key features of your approach?
The key point of my method is global optimization using graph optimization, unlike a conventional Kalman filter or least-squares-based positioning methods. In addition, highly accurate relative position estimation using the time difference of carrier wave phases of smartphones contributed to the accuracy. Because the competition dataset included environments such as tunnels and elevated structures in which GNSS cannot be received at all, I devised an algorithm with two optimization steps (first velocity optimization, then position optimization) and applied it to the competition. This method enables highly accurate position estimation for vehicle driving data in various GNSS signal reception environments using only smartphone GNSS observation data.
What end-user applications are you expecting your approach to enable?
Decimeter-accurate location estimation could lead to lane-level navigation for vehicles, pedestrian navigation, and advanced location-based smartphone games.
Dai
Shubin Dai, Kaggle Community
2nd Place Winner: Improving Smartphone GNSS positioning using Gradient Descent Method
What is your research focus and how does it relate to the contest?
I am a data scientist and one of the top competition grandmasters on Kaggle. My research interests include computer vision, natural language processing, autonomous driving, and reinforcement learning. I placed in the top three in 14 related competitions (13 of which were solo). So, despite my lack of background knowledge in the GNSS field, these methods, skills and experiences helped me find a solution.
How long have you been developing the technology or approach you used to win the contest?
I spent about 50 days on this competition, including learning principles of GNSS and understanding all kinds of algorithms by reading books, papers and source codes. The Kaggle platform is very helpful when we want to get started in a new field.
Have you participated in previous editions of this contest?
I did not participate in the competition held last year, but I learned a lot from solutions of recent years, particularly the third-place solution.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The benchmark datasets include raw GNSS measurement and raw readings from inertial sensors, using smartphones (Xiaomi Mi 8, Google Pixel 4, etc.) enabled with dual-frequency and ADR (accumulated delta range) in driving scenarios, collected in the San Francisco Bay area.
In the GSDC2021 dataset, there are 29 drives with 73 phone GNSS logs in the training set and 19 drives with 48 phone logs in the test set. Compared to 2021’s competition, in the GSDC2022 dataset we can see more data overall and a wider variety of routes: 62 drives with 170 phone logs are provided in the training set and 36 drives with only one phone per drive are provided in the test set.
The drives in the training set took 15 to 60 minutes at an average speed of 18 m/s.
What accuracies were you able to obtain?
According to the metric of this competition, the score is calculated as the mean of the 50th and 95th percentile distance errors. The score on my local validation set is 1.929 m, the score on the public test set is 1.608 m, and the score on private test set is 1.499 m. When we calculate the mean error, the score is 1.401 m on a validated set, the mean error of 40% of the trips are under 1 m. I think the competition metric is more reliable as the 95th percentile distance error is also important.
By the way, my local validation set is more difficult to optimize than the test set, so the mean error on the test set is expected to be lower than 1.401 m.
What are the key features of your approach?
The competition data is noisy due to multipath effects, non-line-of-sight receptions, receiver noise and missing data, therefore it’s quite challenging. I found that the optimal estimation for each point locally is not stable and can be affected by noise at that point on the track. If we can find a solution to a whole track globally, the noise can be reduced as the model must follow all kinds of constraints, such as geometry constraints, speed constrains, and global acceleration constraints.
Although we could extend the WLS and Kalman filter solution to take more points on a track into consideration, it’s not so easy to model all kinds of constrains. On the other hand, if we use a global optimization method, such as factor graph optimization and neural networks, we can add the constrains easily, which makes it more efficient to conduct experiments.
Following the solution of the third-place winner in last year’s competition, I used the global optimization method by taking into account gradient descent, pseudorange, pseudorange rate, accumulated carrier phase (ADR), phone speed and acceleration constraints of every time epoch on a track. When optimizing the track using gradient descent, the losses are designed to filter out abnormal data and reduce the noise by a series of physical and geometrical rules. I spent much time searching for the constraints, proving them and turning them into losses that can be used to update the coordinates iteratively during the competition.
What end-user applications are you expecting your approach to enable?
According to the setting of this competition, we can post-process data collected using Android phones, which is easily obtained. The track obtained can then be optimized using the solutions from this competition. The solutions from the first and the second place can both be considered as a framework that can be extended by adding more constrains to it to improve accuracy.
Everett
Tim Everett, RTK Consultants LLC
3rd Place Winner: An RTKLIB Open-Source-Based Solution
What is your research focus and how does it relate to the contest?
I develop and maintain the demo5 fork of the popular RTKLIB open-source GPS/GNSS software tool. I have optimized this software for low-cost precision GNSS solutions, so it is very closely related to the goals of this competition. My background is in control system theory and I worked in product and technology development for servo systems in the disk drive industry for 25 years before switching to the GNSS field. The mathematics turns out to be quite similar between the two as both are problems in precision positioning, just different in scale. In disk drives, it is nanometers over centimeters and in precision GNSS, it is centimeters over kilometers.
How long have you been developing the technology or approach you used to win the contest?
I have been developing and maintaining low-cost precision GNSS solutions in the RTKLIB software for about six years but have only worked with smartphone solutions in the last year or two.
Have you participated in previous editions of this contest?
I did not participate in last year’s competition but I did work with the data after the contest was over and shared a solution using RTKLIB that would have placed fifth in the competition.
What accuracies were you able to obtain?
I achieved a score of 1.648 m on the private leaderboard. This represents the average of the 50th percentile and the 95th percentile of the errors as scored by Kaggle. Kaggle does not provide any further breakdown of this number but, based on the training data for which ground truths were provided, this corresponded to a 50th percentile error of roughly 0.9 m and a 95th percentile error of roughly 2.3 m. With a small tweak to my solution after the competition was over, I was able to improve my private leaderboard score to 1.593 m, which would have been within 1 cm of the third-place solution.
What are the key features of your approach?
My approach was to use the existing post-processing kinematic (PPK) solution algorithm in RTKLIB but to reoptimize it for the unique characteristics of the smartphone observation data. A PPK solution is the post-processing equivalent of a real-time kinematic (RTK) solution and is a differential solution that relies on differencing the receiver observations with observations from a nearby base station to cancel out most of the largest error sources — including atmospheric, orbital and clock errors — since these errors are common between the two sets of proximate observations.
Because smartphones have very poor GNSS antennas and they were mounted inside vehicles, the signal quality is much lower and the multipath much greater than those for which the RTKLIB algorithm was optimized. In addition, the smartphones were using the L5 frequency band, whereas RTKLIB was optimized for the more commonly used L2 frequency band. One of the main goals of my optimization process was to include many low-quality observations in the solution that would normally be discarded, but to de-weight them appropriately.
What end-user applications are you expecting your approach to enable?
RTKLIB software is currently used to provide precision solutions for many end-user applications such as surveying, drone photogrammetry, sports tracking, precision agriculture, utility location, marine navigation and ground subsistence monitoring. Although smartphones won’t replace dedicated low-cost GNSS receivers, the challenging nature of the smartphone data severely stresses the RTKLIB algorithms and exposes numerous opportunities for improvement that are much less obvious with more typical, higher quality data. I have pulled these improvements into the main branch of the demo5 version of RTKLIB, and hence this work should immediately improve the quality of all these applications and extend their use into more challenging environments.
Photo: Google
Acknowledgements: Thanks to the Institute of Navigation (ION) for co-sponsoring the 2022 Smartphone Decimeter Challenge. Thanks to Luke Walcher and Tolu Ojelade for their contributions to the photos used in this article.
The portable drone has an RTK module for centimeter-level precision and a 56× zoom camera
DJI has launched its Mavic 3 Enterprise Series, designed for business, government, education and public safety.
The DJI Mavic 3E and DJI Mavic 3T are compact drones designed to provide professional users with safe and efficient aerial technology. Both drones are based on DJI’s flagship Mavic 3 series and have been designed to operate in a vast array of commercial missions.
Portable and compact, the drones can be carried in one hand and deployed at a moment’s notice. Flight time is 45 minutes.
Surveying tools. Both models have a real-time kinematic (RTK) module that enables surveying professionals to achieve centimeter-level accuracy with support for network RTK, custom network RTK services, and the D-RTK 2 Mobile Station.
The D-RTK 2 Mobile Station is DJI’s upgraded high-precision GNSS receiver that supports all major global satellite navigation systems, providing real-time differential corrections.
Safety Features. The Mavic 3 Enterprise series has improved obstacle sensing and navigation systems, including DJI AirSense, which receives ADS-B signals from traditional aircraft in the area to warn drone pilots of other air traffic nearby. The new improved DJI APAS system 5.0 for obstacle sensing with zero blind spots is supported by six omnidirectional fish-eye sensors.
Cameras equipped. It integrates a 20-MP wide-angle camera with a 4/3 CMOS sensor with large 3.3 μm pixels that, together with Intelligent Low-Light Mode, offer significantly improved performance in dim conditions. Its powerful up-to-56x hybrid zoom camera has an equivalent focal length of 162mm for 12MP images. A mechanical shutter prevents motion blur and supports rapid 0.7-second interval shooting.
Photo: DJI
The DJI Mavic 3E enables efficient mapping and surveying missions without the need for ground control points. Other fields that could use the drone include environmental and wildlife protection, construction, surveying, energy and public safety.
The DJI Mavic 3T is engineered for aerial operations in firefighting, search and rescue, inspections and night missions. It has the same tele camera as Mavic 3E, a 48 MP camera with a 1/2” CMOS sensor, and a thermal camera with a Display Field of View (DFOV) of 61° and an equivalent focal length of 40mm with 640 × 512 px resolution.
The Mavic 3T’s thermal camera supports point and area temperature measurement, high temperature alerts, color palettes, and isotherms to help professionals find hot spots and make quick decisions. With a simultaneous split-screen zoom, the Mavic 3T’s thermal and zoom cameras support 28× continuous side-by-side digital zoom for easy comparisons.
Image transmission. With a maximum control range of 15 km, DJI O3 Enterprise Transmission enables the Mavic 3 Enterprise drones to fly further and transmit signals with higher stability, offering pilots greater peace of mind during flight. It provides a high frame rate live feed at 1080p/30 fps.
UAVOS has completed a successful test flight of the ApusDuo solar-powered high-altitude platform system (HAPS). The test flight, at a European Flight Center, was conducted continuously for 11 hours and reached altitudes of 15,000 meters.
The ApusDuo successfully achieved more than two dozen test points, including energy balance validation, power and propulsion performance, and propeller revolutions per minute evaluation. The team also tested aircraft motor control efficiency, which was refined following previous test flights.
After operations in Europe, UAVOS plans to transport ApusDuo to Argentina. The company is accelerating preparations to perform the next phase of test flights in the stratosphere.
ApusDuo is a stratospheric UAV running on solar power, and is meant to provide persistent local satellite-like services. Built with carbon-fiber composites, it can be landed, re-equipped with multitask payloads and re-deployed. It is also capable of flying autonomously from takeoff to landing and can be remotely operated from its ground-control station.
