Europe’s EGNOS satnav system has been providing safety-of-life services for 10 years. EGNOS, the European Geostationary Navigation Overlay Service, transmits signals from a duo of satellite transponders in geostationary orbit.
The satellite-based augmentation system (SBAS) gives additional precision to U.S. GPS signals, delivering an average precision of 1.5 meters over European territory, as much as a 10-fold improvement over unaugmented signals. EGNOS also provides confirmation of GPS signal integrity through additional messaging identifying any residual errors.
While the EGNOS Open Service has been in general operation since 2009, EGNOS began its safety-of-life service in March 2011.
The European Space Agency (ESA) designed EGNOS as the European equivalent of the U.S. Wide Area Augmentation System (WAAS), working closely with the European air traffic management agency Eurocontrol. ESA then passed EGNOS to the European GNSS Agency (GSA) to run operationally.
Guiding airliners
EGNOS’s primary customer is aircraft. Without guidance from the ground, pilots using EGNOS can confidently descend in bad weather to 60 meters’ altitude before needing to make visual contact with the tarmac.
On March 17, 2011, France’s Pau Pyrénées Airport was the first airport to use EGNOS. Today, more than 385 airports and helipads and 60 airlines across Europe use EGNOS-based LPV-200 approaches (short for Localizer Performance with Vertical guidance – 200 feet). The EGNOS service requires no ground equipment, and replaces the radio guidance beamed upward by traditional CAT I instrument landing system (ILS) infrastructure with no decrease in performance.
As of March 2021, more than 385 airports and helipads and 60 airlines across Europe are using EGNOS-based LPV-200 approaches. (Image: ESA)
Serving drones
EGNOS is now being eyed as the enabler of unmanned aerial vehicles (UAVs). The GSA has supported numerous trials of drones equipped with EGNOS as well as Galileo through its EGNSS4RPAS project. Crewed aircraft are expected to be vastly outnumbered in our skies by all kinds of UAVs, employed for everything from weather and environmental monitoring to personalized delivery services.
U-Space is Europe’s program to integrate drones into the airspace. (Image: ESA)
The traditional person-based air traffic control model will need to evolve to accommodate such a shift, based on automated monitoring, traffic management and collision avoidance. In Europe, this highly automated version of air traffic control is termed U-space.
EGNOS’s safety-of-life service is essential to making this happen, moving from today’s situation — where drones are limited to specific air corridors and line-of-sight operations — to let them roam freely but safely in busy airspace and built-up areas.
“The whole idea behind EGNOS’s safety-of-life has been to render satellite navigation sufficiently reliable for any kind of use,” explained Didier Flament, who leads ESA’s EGNOS team. “After 10 years of faultless operations, new applications are becoming plain. Drone flight is one example. EGNOS is also being evaluated for train positioning as well as assisted and autonomous automobile driving.”
EGNOS, the next generation
ESA retains responsibility for the system’s evolution, and the middle of this decade should see the debut of its new generation, EGNOS v3.
“While the current system only works with single-frequency GPS signals, EGNOS v3 will operate on a multi-frequency, multi-constellation basis, able to augment all available satellite signals in both L1 and L5 bands, including Galileo,” Didier said. “The result will be far enhanced performance and reliability.
“In addition, we are working with developers of other SBAS around the globe to ensure they stay fully interoperable so for instance EGNOS-equipped aircraft can fly between continents on a seamless basis. Such interoperability, combined with the arrival of the other SBAS systems under development in other regions, will lead to a quasi-global worldwide safety-of-life service coverage in the year 2030.”
Operational and planned satellite-based augmentation systems (SBAS) around the globe. (Image: ESA)
Oxford Technical Solutions has released the xNAV650, the latest in its line of inertial navigation systems (INS), suitable for use on drones.
INS provide surveyors with absolute position, timing and inertial measurements (heading and pitch/roll) that they can integrate into their survey projects. The measurements, when combined with data from other devices (such as lidar sensors and cameras), can greatly enhance the surveying process, leading to a greater return on investment, according to the company.
The xNAV650 is OxTS’ smallest, lightest and most affordable INS to date. It combines 20 years of navigation experience with the latest micro-electromechanical (MEMS) inertial measurement unit (IMU) technology and survey-grade GNSS receivers.
UAV Guidance
The xNAV650 provides highly accurate and reliable measurements – even when payload size and weight are imperative to consider, including for use with unmanned aerial vehicles (UAVs). It measures 77 x 63 x 24 mm and weighs 130 grams.
The xNAV650 INS is suitable for a wide range of UAV data-collection applications, including surveys of bridges, buildings, forests and rail; coastal monitoring; map creation and pipeline exploration.
OxTS’ partner Dronezone used the xNAV650 INS and a Velodyne VLP-16 lidar on a drone to conduct a scan of an aging bridge to look for structural and potential hazards from overgrown foliage.
By fusing the timing, position and inertial data from the INS with the raw data of the Velodyne VLP-16 (using OxTS’ lidar georeferencing software OxTS Georeferencer), the surveyor was able to produce a highly accurate 3D point cloud of the bridge. Fusing the position and inertial data from the xNAV650 INS with the Velodyne VLP-16 lidar data provides a high level of clarit, which can be seen in the foliage, electricity lines and side of the bridge.
The resulting point cloud has enabled the engineers to easily and accurately pinpoint areas of the bridge that need closer attention.
Side view point cloud of bridge. Data collected using and OxTS xNAV650 INS and Velodyne VLP-16 lidar. Data processed using OxTS Georeferencer. (Image: OxTS)
NAVsuite Software
Data from OxTS INS can be fused with the data from almost any lidar sensor. Using OxTS Georeferencer software, point clouds can be georeferences from lidar units specifically from Velodyne, Hesai and Ouster sensors. Work is underway to integrate new lidar sensors from an even wider range of manufacturers into OxTS Georeferencer – allowing OxTS INS users to build a full navigation solution where much of the integration work is already taken care of.
OxTS NAVsuite software is included with all OxTS INS. The full range of software tools allows users of OxTS’ devices to configure and post-process data with ease.
Other optional software features are also available, including Precision Time Protocol (PTP) and GX/IX tight-coupling technology. PTP allows for a much simpler lidar survey set-up over ethernet while simultaneously stamping out time-drift by utilizing the high-quality INS clock source – GNSS. GX/IX tight-coupling technology, OxTS’ own proprietary navigation engine, ensures that users of OxTS Inertial Navigation Systems receive the most accurate measurements possible even in tough GNSS conditions.
A roundup of recent products in the GNSS and inertial positioning industry from the January 2021 issue of GPS World magazine.
OEM
Receiver board
Enhanced with corrections
Photo: Septentrio
The AsteRx-m3 Sx OEM board dual-antenna receiver combines Septentrio’s latest core GNSS technology with the SECORX-S sub-decimeter correction service to enable plug-and-play positioning. High-accuracy positioning is available directly out of the box, GNSS corrections automatically streamed to the receiver. This significantly simplifies the set-up process and eliminates the need for corrections service subscription and maintenance. Corrections are delivered via internet or L-band satellites, ensuring sub-decimeter service even in remote locations where there is no easy internet access.
The new TW5382 smart GNSS antenna is designed for high-accuracy 5G timing. The TW5382 is a multi-band, multi-constellation 5G smart GNSS antenna/receiver that provides 5 ns (1-sigma, clear sky view) timing accuracy. It consists of two components: a Tallysman GNSS Accutenna technology antenna and a professional-grade GNSS timing receiver module. Accutenna supports the full bandwidth of the TW5382 receiver, strong multipath mitigation and deep filtering in a compact IP69K enclosure. These features enable the antenna to provide a strong, pure, in-band, right-hand circular polarized signal to the receiver. The TW5382’s professional-grade multi-constellation and multi-signal timing receiver tracks GPS/QZSS (L1/L2), GLONASS (G1/G2), Galileo (E1/E5b), and BeiDou (B1/B2) signals.
The new Precision GNSS Module (PGM) is designed to offer fast evaluation and a quick path to production for those requiring a precise positioning solution. It is available in a simple-to-use, industry-standard mPCIe (mini peripheral component interconnect express) format and is designed specifically for Swift’s Starling positioning engine running on a host application processor to deliver real-time precision navigation. The PGM utilizes STMicroelectronics’ TeseoV chipset in Quectel’s multi-constellation, dual-band LG69T-AP receiver to create an affordable, easy-to-use solution for customers building industrial, last-mile and internet of things (IoT) platforms. This solution operates with the highest accuracy when used with Swift’s Skylark positioning service.
CAST Navigation tested Emcore’s SDN500 inertial navigation system (INS) in an ultra-high-altitude flight simulation and achieved success. The test required simulating performance at an altitude of more than 24,000 meters and velocities over 600 m/s. Only a few aircraft in the world have such capabilities, including the SR-71 Blackbird, but it is not practical to participate in a test flight on the SR-71. Simulating the SDN500 INS test flight to specific customer profiles on a CAST system is straightforward and cost-effective. Emcore relies on GNSS/INS simulators for hardware-in-the-loop testing to verify the expected performance of algorithms. Emcore sought to validate the velocity and altitude limits of a new GNSS receiver along with the algorithm performance in a tactical-grade SDN500 system.
The Dimensity 700 5G smartphone chipset is a system on chip (SoC) designed to bring advanced 5G capabilities and experiences to the mass market. MediaTek’s Dimensity family of 5G chips is designed to give device makers a suite of options for 5G smartphone models. The chips range from flagship and premium to mid-range and mass market devices to make 5G more accessible for consumers everywhere. GNSS signals received include GPS L1CA and L5, BeiDou B1I and B2, GLONASS L1OF, Galileo E1 and E5, QZSS L1C and L5, and NavIC.
IHawk allows users to inspect sites remotely and then download and view the analysis anywhere in the world. It eliminates the need for engineers to climb towers for inspections or work in hazardous environments. The imagery and information gathered provides a detailed and highly accurate analysis of the condition of power transmission towers.
The Alpin UAS is a long-range, heavy-lift unmanned helicopter capable of carrying up to 160 kg with a range of up to 840 km. The UAS includes a wideband satellite communication channel from its command-and-control station — a valuable feature, particularly for operations in remote areas. The Alpin unmanned helicopter is able to withstand severe weather conditions, carry multiple payloads, and transmit real-time information to defense forces and decision-makers in the field. Its system autopilot has features and advantages such as fully autonomous take-off and landing, remote ground-control network capability, auto-rotation landing capability and high efficiency flight control based on a total energy control system (TECS).
LineVision Online now provides enhanced support for visualizing and mapping DJI drone video camera metadata and field-of-view projections. The secure web application is designed for immersive mapping, analysis, search, sharing and archive of geo-referenced videos, full-motion video, photos and other survey, inspection and surveillance datasets. With enhanced camera metadata mapping in LineVision Online, DJI drone videos can now display a dynamic, field-of-view outline representing where the gimbal camera was looking on the Earth as the video plays in the web-based map interface. Users can select any point along the UAV’s flight track on the map to immediately cue the video to play what was recorded at that location click point.
The Agras T20 drone can conduct autonomous operations over a variety of terrains, such as broad-acre farmlands, terraces and orchards. As a comprehensive spraying system, the T20 allows users to easily set flight and operation parameters. With a built-in real-time kinematic (RTK) centimeter-level positioning system and RTK dongles, centimeter-level waypoint recording is enabled, strengthening operations and ensuring precision spraying.The T20 is equipped with eight nozzles and high-volume pumps that can spray at a rate of up to 6 liters per minute. A highly optimized wind field produces droplets of the correct size and consistency. The T20 is also equipped with a new four-channel electromagnetic flow meter, which monitors and controls four hoses individually, ensuring an efficient flow rate for each nozzle.
A new virtual base station (VBS) feature is available in Qinertia, GNSS and inertial navigation system (INS) post-processing software. Trajectory and orientation are greatly improved by processing inertial data and raw GNSS observables in forward and backward directions. The VBS computes a virtual network around a project in which position accuracy is maximized, homogeneous and robust, such as a PPK short baseline. Once surveyors collect data, Qinertia chooses the most relevant reference stations, builds a virtual network and brings the project to centimeter-level accuracy with no convergence effects, even in urban areas.
Enview Explore is a powerful web application that leverages artificial intelligence and cloud computing to automatically process 3D data at a high speed and scale. Enview performs a variety of geospatial operations, including object recognition, feature extraction, feature-based change detection, and 2D/3D measurement. Enview’s technology has been deployed on thousands of square miles worldwide to protect vital infrastructure and support mission-critical operations. Its unique method for classifying 3D data reduces time to action by focusing on finding meaningful insights.
PDGrade — a machine guidance and positioning system that uses GNSS for pile driving applications — is now optimized for the solar industry with an increased capability in pile installation and navigation accuracy. It removes the need for surveying piles and reviewing as-built information by centralizing all relevant information and providing necessary details to operators and site supervisors.The system features both software and hardware applications to provide operators with detailed information such as pile navigation, pile location, positioning and height information, project progression tracking, and detailed accuracy. The PD machine is fitted with Carlson sensors and a ruggedized Windows-based MC10 tablet. The entire system is then calibrated within PDGrade.
Advancements in sensors, cameras and automation have fueled the growth of the aerial imaging industry, which is expected to reach $2.83 billion by 2022.
By Swamini Kulkarni
Unmanned aerial vehicles (UAV), or drones, often gain the spotlight with to their ability to capture the view from a vantage point. For years, airborne cameras have clicked never-seen-before pictures across planet. Now imaging technology is utilized to monitor natural calamities and borders of countries.
Drones have been quickly adopted in various industries including surveillance, geospatial mapping, post-disaster monitoring, and even entertainment. The advancements in sensors, cameras and automation have fueled growth of the aerial imaging industry.
Cameras mounted on balloons, kites and now drones are used widely across various verticals such as government, agriculture, civil engineering and research. Surveillance through satellite imagery has challenges, many of which drones can overcome. Drones can be used whenever we want and can be equipped with lidar systems, geographic information systems and advanced cameras. This has created lucrative opportunities in the aerial imaging industry.
According to Allied Market Research, the global aerial imaging market is expected to reach $2.83 billion by 2022, growing at a CAGR of 12.9% from 2016 to 2022. The launch of novel and intuitive cameras has further increased the popularity of aerial imaging.
Advent of novel, intuitive cameras for aerial imaging
AirSelfie, a prime market player in the aerial imaging industry, launched AIR PIX aerial camera at Consumer Technology Association (CES) 2020. The company announced that it has started shipping AIR PIX+ to customers the world’s smallest pocket-sized aerial camera. Moreover, it declared that it would make available AIR DUO, the aerial camera equipped with the dual parallel camera later in 2020. Both of these cameras offer state-of-the-art technology and would prove to be vital in aerial imaging and capturing videos from the air.
Skydio, the leading U.S. manufacturer of drones and autonomous flight technology, recently launched new software solutions and autonomous drone platform for situational awareness and inspection. It is observed that despite the potential drones showcase in aerial imaging, its adoption is still limited due to concerns regarding the risk of crashes of autonomous drones.
Moreover, the requirement to hire experienced pilots and data security concerns prevent firms from scaling their aerial imaging programs. That’s why Skydio aims to unlock the potential through this autonomy software and change people’s perspective toward drones.
In addition, the company has partnered with Eagleview, a leader in aerial imagery industry and data analytics to empower home insurance agents to offer accurate inspection of residential homes without the use of expert drone pilots. This technology is expected to be available in the fourth quarter of 2020.
Artificial intelligence: Future of aerial imaging
Today, every industry is searching for ways to operate devices remotely or at least with minimum physical contact. With the experience of global pandemic keeping in mind, the future is clearly bright for autonomous drones.
Several industries, including aerial imaging, rely on advancements in autonomous UAVs. Moreover, the success of aerial imaging depends on both autonomous drones and carefully dealing with the data gathered by aerial cameras. This is where artificial intelligence (AI) comes into the picture.
For use of aerial imaging for property surveillance, there is a dire need for a solution that can streamline data analysis, make sense of the data gathered by cameras, and scale up the level of details offered by aerial imaging.
AI-based aerial imaging can be used for automated property analytics and streamline facilitation of risk underwriting and claim management. Moreover, it can offer datasets to improve risk modeling. AI-powered aerial imaging technology can leverage AI to detect changes in property evaluation, which can benefit public safety and city planning.
COVID-19 increases data demand
We live during a period of drastic change. The COVID-19 pandemic has influenced almost every industry across the globe and has increased the demand for quality of data despite a lack of resources. Moreover, there is a need for faster and better data analysis to help industries scale up. The incorporation of AI and aerial imaging can benefit organizations to scale up their operations and streamline their processes at affordable costs.
Nearmap, a prominent aerial imagery company, has launched its innovative Nearmap AI for automatic aerial imagery insights at scale. This technology is the first among aerial imagery to offer AI analysis along with high-definition aerial images on a commercial scale. Moreover, it enables customers to automatically detect ground features and verify insight against aerial imagery at a larger scale.
It is clear that the use of aerial imaging will increase in the future. Moreover, the integration of AI in aerial imaging will help organizations to scale up their business and aid in data analysis to gain valuable insights.
It is safe to say that the aerial imaging technology has changed over time, but the desire of humans to see the world from a high above has been constant, which is exactly what should keep aerial imaging technology profitable in years to come.
Allied Market Research is offering a market report on aerial imaging.
Swamini Kulkarni
Swamini Kulkarni holds a bachelor’s degree from Pune University, India, and works as a content writer.
NXP and Auterion join forces to enable next-generation secure drone fleets with automotive certified solutions, high-reliability networking, and a scalable and open software platform.
Working together, the companies aim to develop highly reliable and advanced hardware and software solutions deployable in an unmanned aerial vehicle.
With the development of regulations and the increasing number of autonomous systems in the field, the requirement for components and software that are certifiable and the ability to deploy intelligence on the edge is becoming more and more important.
NXP provides semiconductor components and expertise leading to certifiable electronics solutions, including computational horsepower, secure element for encryption and authentication, and high reliability automotive networking.
Auterion is offering the hardware reference design and Auterion Enterprise PX4, the software for the flight controller and the mission computer to make drone fleets safe and fully integrated into workflows. Auterion is the largest contributor to PX4 and builds its software platform on open standards, ensuring that enterprises have access to a managed and tested distribution of the open source technology.
The partnership addresses the needs of the unmanned aerial vehicles industry for compatible hardware and software solutions that will help drone manufactures bring state-of-the-art products to market. The aim is to ensure that manufacturers have a streamlined path to certification and are connected to existing workflows.
“This partnership will enable the mobile robotics community with the components meeting quality specifications needed to ensure functional safety and security in drones and rovers based on reliable long life industrial and automotive parts and reference designs,” said Iain Galloway, Drone Program Lead, Systems Innovation, NXP. “We have been participating in the open source PX4 community for several years now and with this close relationship with Auterion, and Auterion Enterprise PX4, we are excited to work together to ensure these vehicles are prepared to meet current and future regulations and standards governing modular safe drone architectures.”
“Safety is the number one priority in commercial drone operations. NXP’s leading position as a semiconductor provider for safety-critical automotive applications is the perfect pairing for Auterion’s enterprise-grade drone software platform,” said Lorenz Meier, co-founder and CEO, Auterion. “Together, we will be able to provide integrated hardware and software solutions to the drone industry that combine high-performance compute with safety-first engineering.”
NXP and Auterion will collaborate on the core hardware and software components of an autonomous system, this includes, but is not limited to, the following topics:
Developing the next generation Auterion Skynode avionics module reference design, based on the latest Pixhawk autopilot Reference Standards and on the NXP i.MX 8M Mini as a companion computer, and on future components in this family.
Integrating navigation modules incorporating NXP Ultra-Wideband (UWB), automotive MCU, NFC and authentication for precision landing applications.
Developing Battery Management System (BMS) solutions based on the latest Pixhawk Smart Battery Standards.
Developing Automotive CAN and CAN-FD node solutions supporting popular software protocols such as UAVCAN and MRCAN for mobile robotics peripherals.
Collaborate in the data cybersecurity and drone regulatory space to help shape and meet future regulations.
Both parties will continue to support the PX4 open source community and upstream PX4 development, in an effort to enable the whole industry.
Organized by Professor Zak Kassas from the University of California, Riverside, the workshop will feature presentations and panels by experts and leaders from government (National Science Foundation, Office of Naval Research, Air Force Research Laboratory, Department of Transportation), industry (Google, Daimler, and Ford) and academia (The Ohio State University, UC San Diego, University of Southern California).
The workshop will discuss opportunities and challenges (technical, commercial, ethical, and legal) associated with developing fully autonomous systems that are cognizant and trustworthy for safety-critical applications. Examples include unmanned aerial vehicles (UAVs), self-driving cars and unmanned underwater and surface vehicles.
Kassas, director of the Autonomous Systems Perception, Intelligence, & Navigation Laboratory (ASPIN), leads a team of researchers developing reliable and accurate navigation that exploits existing signals of opportunity, rather than GPS, to meet the stringent requirements of fully-autonomous systems, such as UAVs and self-driving cars.
The u-blox ZOE-M8Q is designed for wearables, UAVs and asset trackers. Photo: U-blox
U-blox has launched a new positioning module, the ZOE-M8G. The ZOE-M8G is an ultra-compact GNSS receiver module designed for markets where small size, minimal weight and high location precision are essential.
ZOE-M8G offers exceptionally high location accuracy by concurrently connecting to GPS, Galileo and either GLONASS or BeiDou. It also provides -167 dBm navigation sensitivity, important for wearable devices, unmanned aerial vehicles (UAVs) and asset tracker applications.
The new u-blox ZOE-M8G helps simplify product designs, because it is a fully integrated, complete GNSS solution with built-in SAW-filter and Low Noise Amplifier (LNA). It can be used with passive antennas without the need for additional components, and doesn’t compromise performance.
The ZOE-M8G GNSS module measures 4.5 x 4.5 x 1.0 millimeters. Due to its small size, a complete GNSS design using a ZOE-M8G module takes approximately 30 percent less printed circuit board (PCB) area compared to a conventional discrete chip design with a CSP chip GNSS receiver.
“When you’re designing products such as smart watches, fitness trackers, asset trackers, UBI dongles and even drones, every square millimeter and every gram counts. The u-blox ZOE-M8G makes it significantly easier for product designers to achieve precise location tracking while keeping within their strict form factor and weight restrictions,” said Uffe Pless, product marketing, Positioning Product Center at u-blox.
Samples of the u-blox ZOE-M8G will be available in February 2017, and volume production will start in October 2017.
Lockheed Martin and the Warsaw University of Technology (WUT) successfully demonstrated their UAV optimization technologies using aerial command and control (C2) of multiple unmanned aerial vehicles (UAVs).
The demonstration marks a successful milestone in the joint WUT-Lockheed Martin advanced applied research program on optimization of diverse fleets of aircraft, and concepts associated with manned-unmanned command and control of airborne platform systems.
“These technologies have tremendous commercial and military potential as the world moves toward greater and greater use of unmanned aerial systems,” said Prof. Janusz Narkiewicz, head of WUT’s Department of Automation and Aeronautical Systems. “Understanding how different assets can interoperate, communicate and serve common objectives with maximum efficiency is a challenging task in the growing field of UAV technologies.”
Through the use of advanced mathematic calculations and a systems-of-systems approach, the technology bolsters mission efficiency by adapting the fleet’s commanded flight paths, speeds, division of duties and sensor performance. Modeling all the constraints of the task at hand, the students calculate the “best answer,” usually beating either the human best guess or simpler approaches by 10 to 20 percent.
The goal of the team’s latest project was to advance previous optimization work by incorporating airborne C2, improving user interfaces, and testing new methods for related subroutines. With a vision of ultimately developing fast dynamically adaptive approaches to live management of a UAV fleet, this work is an important contribution to the concept of manned-unmanned teaming, where manned assets operate seamlessly with surrogate UAVs, often controlling many at a time against specific tasks.
The technology demonstrates that, with the right tools, an operator may adapt to changing scenarios, calculate new solutions, and deploy those new, optimized solutions to the fleet of commanded aircraft, whether for civil or military purposes, a Lockheed Martin news release said.
The recent demonstration can be equated to a search-and-rescue task, where every minute shaved off of a search pattern could be the difference between life and death.
In another example, if UAVs were to be used to deliver small packages to consumers, the 10 to 20 percent performance improvement could be the competitive edge that keeps an operation in business ahead of the competition.
The program builds on the strong industrial and academic partnership between Poland and Lockheed Martin aimed at motivating young Polish engineers to address tomorrow’s defense and industrial needs. WUT and Lockheed Martin are seeking new Polish partners to further advance Polish research and development capabilities on manned-unmanned airborne platform system integration.
Unmanned aerial vehicles (UAVs) — both their design and their many applications — are the topic of GPS World‘s May webinar. The free webinar is scheduled for Thursday, May 19, at 1 p.m. EDT. Register here.
The webinar, sponsored by Septentrio, will engage you in discussions involving:
Read the full details of each of the speakers’ presentations below.
Dennis Akos, Professor, University of Colorado at Boulder
Subtopic 1: GNSS Robustness for Unmanned Aircraft Systems Presented by Dennis Akos, professor, University of Colorado at Boulder, and Joshua Stubbs, Ph.D. candidate
When siting the antenna of a GNSS receiver or designing a GNSS-based navigation system, electromagnetic compatibility is an important concern. This is particularly true for airborne platforms. Akos discusses how radio-frequency interference can impact GNSS equipment on unmanned aircraft systems and how robustly the equipment can navigate those systems.
Joshua Stubbs, Ph.D. candidate
Subtopic 2: Autonomous Relative Navigation Presented by Dr. Jeff Fayman, CTO, Geodetics
Future UAVS will require relative navigation capability to fulfill a broad range of assisted manned and unmanned missions. A new approach, demonstrated in application to aerial refueling, provides access to accurate relative time-space positioning information (R-TSPI) between platforms.
Subtopic 3: UAV Operation in Industrial Environments Presented by Roy Jeunen, founder, AiRobot
The distance from an in-flight UAV to the industrial asset that it is observing or inspecting obviously has critical importance for safety, data precision and cost-effectiveness. The AiRobot Ranger counters this problem by displaying the distance between the UAV and the object of interest on multiple smart phones or tablets, ensuring the extra situational awareness that is crucial for professional UAV operations.
Jan Leyssens, Product Manager, Septentrio
Subtopic 4: Practical Tips on How to Avoid Problems While Integrating High-Accuracy GNSS Receivers Aboard UAVs Presented by Jan Leyssens, product manager, Septentrio
Register today.If you can’t attend the live event, you are invited to still register — you will be sent the on-demand version 24 hours after the event concludes. The on-demand version will be available until May 19, 2017.
When siting the antenna of a GNSS receiver or designing a GNSS-based navigation system, electromagnetic compatibility is an important concern. This is particularly true for airborne platforms. In this month’s cover story, we take a look at how radio-frequency interference can impact GNSS equipment on unmanned aircraft systems and how robustly the equipment can navigate those systems.
INNOVATION INSIGHTS with Richard Langley
WHAT’S THE WEAKEST THING ABOUT GNSS?Literally, it’s the signals. The strength of GNSS signals is notoriously low as anyone who has tried to operate a consumer-level device inside a steel and concrete building can readily attest. Unlike mobile phone signals, GNSS signals are too weak to survive the attenuation of walls, floors, and ceilings and so typically cannot provide a dependable signal indoors for most receivers.
Even outdoors, the signals can be significantly attenuated by dense wet foliage and completely blocked by buildings and other objects. The GPS C/A-code signal generated by the transmitter in a satellite is approximately 27 watts. If such a transmitter were operated on Earth it would provide a decent signal even inside a nearby building. First responders, for example, can communicate with each other using portable transceivers with even lower-powered transmitters.
However, GPS satellites are about 20,000 kilometers away at their closest and the signals they transmit spread out as they travel to the Earth and even with the directivity of the satellite transmitting antenna, by the time the signals reach the surface of the Earth, their power density is only on the order of 10-13 watts per square meter. And that’s outdoors.
This signal is so weak that it is buried in the receiver’s background noise, which is similar to what you hear when you tune an AM radio between stations. So how can GPS possibly work with such a weak signal? The received signal is actually spread out over several megahertz of radio-frequency spectrum by the pseudorandom noise ranging code. It is this known noise-like code that allows receivers to determine the biased-ranges to satellites and from those ranges determine their positions. Knowing the code, the receiver de-spreads the weak received signal, concentrating it and lifting it above an acceptably low background noise.
All is fine and well as long as the received signal density doesn’t drop much below the 10-13 watts per square meter level but also the background noise level mustn’t rise much above the acceptable level for which the receiver is designed. Both of these criteria are reflected in the carrier-to-noise-density ratio, or C/N0, of the signal. Why might the noise level change? The noise comes from the receiver itself as well as from naturally produced electromagnetic radiation from the sky, the ground, and objects in the receiving antenna’s vicinity. The sky noise includes so-called cosmic noise from the sun, Milky Way galaxy, other discrete cosmic objects and radiation left over from the Big Bang as well as radiation from our atmosphere. For the most part, the noise from these sources is small but occasionally the sun can have a radio outburst that can significantly increase the noise level at GNSS frequencies and actually overpower the GNSS signals as happened with GPS in December 2006.
But the noise level can also be impacted by human-made electrical devices in the vicinity of a GNSS receiver’s antenna. This radio-frequency interference, or RFI, can come from devices such as radio transmitters, microwave ovens, motors, relays, ignition systems, switching power supplies and light dimmers. So, when siting the antenna of a GNSS receiver or designing a GNSS-based navigation system, electromagnetic compatibility is an important concern. This is particularly true for airborne platforms. In this month’s column we take a look at how RFI can impact GNSS equipment on unmanned aircraft systems and how robustly can the equipment navigate those systems.
As the number of unmanned aircraft systems (UAS; also called unmanned aerial vehicles and drones) in use is increasing across many sectors, there is an interest in understanding the robustness of the GNSS engine used on UAS. With UAS being integrated into the National Airspace System (NAS), questions arise about what kind of navigation system should be used on UAS, and to what degree it should be standardized. Conventional aircraft typically use a certified GNSS receiver for navigational purposes, and as UAS will share the sky with conventional aircraft in the future, it is not unreasonable that UAS will use similar receivers.
The first part of this article provides background on the status of GNSS standards for UAS. In the second part, we discuss why radio-frequency interference (RFI) can be expected on some UAS, together with what issues the RFI could cause for the GNSS engine. A simple experiment to determine the presence of RFI in the GPS L1 band due to proximity of a GPS antenna to electronics is presented in this section as well. The third part of the article discusses real-time kinematic (RTK) positioning for UAS purposes. In terms of accuracy, RTK positioning often provides the best results. The robustness of RTK measurements is questionable, though, because the technique relies on carrier-phase measurements. We present a case study, which shows some of the issues of using RTK positioning for UAS, in this part of the article, too.
GNSS standards for UAS
GNSS, and especially GPS, have been used in aviation for quite some time. The GPS receivers used for aviation have to guarantee a certain level of performance to be used, and are certified by the manufacturer to deliver said performance.
The Federal Aviation Administration (FAA) is working on integrating UAS into the NAS. The development of UAS has been quick and has led to a lack of standardization for UAS, something that does exist for traditional manned aircraft. This has led to operators in most cases having to file for exemptions from the existing rules in order to use UAS. It is the ambition of the FAA to transition from issuing exemptions to issuing certifications of UAS once an agreement on regulations has been reached. There are still a number of challenges associated with a full integration of UAS into the NAS, including regulatory, procedural and technical challenges.
The Wide Area Augmentation System (WAAS) was the first operational space-based augmentation system, intended to increase the robustness and reliability of GPS for aviation purposes. The WAAS Minimum Operational Performance Standards (MOPS) document (see Further Reading) specifies what kind of performance GPS plus WAAS provides to aviation users.
The MOPS requirements have been carefully examined and extended. The maximum in-band interference levels for aviation have been theoretically analyzed. As long as signal and interference levels are within the specified ranges, the required performance should be expected.
These levels, combined with the WAAS MOPS, provide the aviation community with the standardization required for manned aircraft operations where lives can be at stake if something were to go wrong with a navigation system. A Volpe National Transportation Systems Center report (see Further Reading) recommends the use of certified GPS receivers for applications where GPS is a critical system. This is not yet a requirement for UAS, and the question remains unanswered as to whether this will be a requirement for UAS in the future.
Traditional aviation uses required navigation performance (RNP), a performance-based navigation approach, to assess what type of navigation systems can be used for different phases of flight. For example, while an aircraft is en route, an RNP of 2 nautical miles is required, meaning the actual position of the aircraft cannot deviate more than 2 nautical miles from a reported position. It should be noted that RNP takes the entire system into consideration, from the space-segment to the receiver to the capabilities of the aircraft.
GNSS receivers used on manned aircraft have to be certified to deliver the RNP for each phase of flight for which they are used. Receiver autonomous integrity monitoring (RAIM) is used to ensure that faulty measurements do not affect the position and navigation solution. Due to the nature of RAIM, more satellites are required than the traditional minimum of four. If GNSS supplements other systems on board the aircraft, RAIM may be used to only monitor the quality of the system, and it will report when performance is below the required minimum. This form of RAIM requires a minimum of five satellites.
However, if the aircraft depends on GNSS for navigation, RAIM must be able to determine if a particular satellite is providing incorrect or subpar data. This requires one additional satellite, bringing the minimum number of satellites that have to be in view of the receiver’s antenna up to six (two more than non-RAIM GNSS operation).
However, using RAIM requires additional computational power, which one might not be able to provide on board a UAS due to size, weight and power limitations. It has been suggested that a GNSS system coupled with an inertial navigation system (INS) could be used for UAS navigation. A micro-electro-mechanical system (MEMS) INS would be very small, would not require a lot of power, and could improve the performance of a UAS navigation system. A GNSS plus MEMS INS approach may well be able to provide the robustness needed for UAS. However, the analysis of such a system is outside the scope of this article.
Some basic considerations should be taken into account for a UAS GNSS positioning system. Integrity should be prioritized over accuracy if the system is used for navigational purposes. Low-altitude operations could bring on problems of sky blockage. The proposed solution to this is to use a receiver capable of using multiple constellations to ensure that as many satellites as possible are in view.
Radio frequency interference
Radio frequency interference, or RFI, is the interference caused by electromagnetic waves interacting with a system they were not intended to interact with. A familiar case of RFI can be experienced when a cellular phone is placed in close proximity to an AM radio. A distinctive sound can sometimes be heard, which is the sound of RFI interacting with the radio.
Many forms of RFI exist. The interference can be in-band, that is, originating on frequencies transmitted within the band occupied by a desired signal, or out-of-band where the center-frequency of the interfering signal lies outside the band used by the desired signal but it can have a nonlinear impact on the components in the front end of the GNSS receiver. In some cases. the bandwidth of the interference is very small (narrowband), and in some cases the bandwidth is quite large (broadband). Depending on the type of interference, the affected systems will react differently.
RFI can, for obvious reasons, be expected from intentional radiators, such as equipment broadcasting signals near the GNSS signal frequencies, or other equipment that emits harmonics that lie close to the GNSS frequencies. These sources are documented, and the effects of them can be mitigated through proper planning and analysis.
However, electrical equipment can produce RFI that is not intended to be emitted — a so-called unintentional radiator. The Federal Communications Commission (FCC) Part 15 regulations define an unintentional radiator as “a device that intentionally generates radio frequency energy for use within the device, or that sends radio frequency signals by conduction to associated equipment via connecting wiring, but which is not intended to emit RF energy by radiation or induction.” Such devices are allowed to emit signal levels up to 300 or 500 microvolts per meter (depending on the class of the device) in the GNSS bands, as measured three meters away from the device.
Although most GNSS frequencies are protected, the risk for intentional or unintentional RFI exists. Some elements of the GPS system have been designed to mitigate interference effects, and GPS remains a relatively robust system. However, there are still sources that could interfere with the GPS signals, such as out-of-band transmissions, harmonics of airborne or ground-based transmitter equipment, radar transmitters or even local oscillators in nearby equipment.
In 1996, under a presidential decision directive, a commission to investigate a broad range of infrastructure vulnerabilities, including vulnerabilities to GPS, was set up. The commission found that GPS is in fact vulnerable to unintentional disruptions, from both human-made and naturally occurring sources. The commission recommended using certified GPS receivers for critical applications. The commission further recommended monitoring, reporting and locating unintentional RFI sources.
One of the potential issues with RFI in a GNSS engine is that it can cause false local correlation peaks, which could cause the code-tracking loop and the carrier-tracking loop to diverge from the main correlation peak.
RFI in the UAS GNSS Engine. On smaller UAS, space restrictions could lead to electronic components being placed in close proximity to each other. As stated earlier, some of these components could be producing RFI in the GNSS bands. If the RFI is strong enough to significantly raise the noise floor, the GPS signals could effectively be drowned out by noise. UAS that rely primarily on GNSS for navigation will risk losing navigational capabilities during such occurrences.
With no external interference present, the noise floor should be at the receiver’s thermal noise floor. The presence of interference could be indicated by the raising of the noise floor above the level of the thermal noise.
FIGURE 1 shows a simple setup for testing the hypothesis that electronics found on a common UAS could produce harmful RFI in the GPS engine. Some of the onboard equipment was a flight-controller, a 915-MHz communication link and a 2.4-GHz communication link.
FIGURE 1. Setup to test for GPS RFI.
A GPS antenna was placed outside and inside the UAS at common antenna locations. The antenna was connected to a high-performance GPS single-frequency-receiver evaluation kit and a spectrum analyzer. To enhance the effects and signals, a 40-dB inline amplifier was connected before the signal was split.
Three tests were carried out in this case study:
In a reference test, the antenna was placed on the outside of the airframe and the UAS was not powered on.
With the UAS power remaining off, the antenna was placed inside the airframe to see how much the signal was attenuated (see FIGURE 2).
With the antenna still inside the airframe, the UAS was powered on and all systems on the UAS were running.
FIGURE 2. Inside the UAS (including the GPS antenna).
The results from the receiver can be seen in FIGURES 3 and 4. Figure 3 shows that the number of satellites being tracked by the GPS receiver did not change between tests.
FIGURE 3. Satellites tracked by the evaluation-kit receiver.FIGURE 4. C/N0 values for different antenna and power configurations.
However, Figure 4 shows C/N0 for each test, and a clear difference can be seen (up to 10-dB difference from the case where the antenna was in the same location but with the UAS on and off). While this difference did not affect the receiver’s ability to provide a position solution, the accuracy was likely degraded due to the RFI. In a real-world scenario, this could lead to the user not noticing the presence of RFI, since the receiver is still able to output a position.
TABLE 1 shows some metrics calculated from the GPS receiver data. The table clearly shows a drop in C/N0 values when the UAS is powered on.
Table 1. Calculated values.
The results from the spectrum analyzer further show the effects of turning the UAS and its equipment on. FIGURE 5 shows the frequency spectrum using an average of 50 sweeps centered at 1575.42 MHz (GPS L1) with a bandwidth of 30 MHz for the case when the antenna was inside the airframe and the UAS was switched off. Due to improper initial calibration, the absolute values of the measurements are incorrect, and should be increased by 9 dBm. However, the relative measurements are still valid. FIGURE 6 shows the same setup for the spectrum analyzer but with all the UAS equipment on with the same caveat about the absolute values.
By comparing Figures 5 and 6, it is clear that the noise floor rises significantly when the UAS and its equipment is switched on. The GPS “bump” that was visible in the center of Figure 5 is no longer visible when the UAS is switched on in Figure 6.
FIGURE 5. RF spectrum when the antenna is inside the airframe, UAS switched off. See text concerning y-axis scale.FIGURE 6. RF spectrum when the antenna is inside the airframe, UAS switched on (all systems running). See text concerning y-axis scale.
RTK Positioning
RTK positioning is a high-accuracy GNSS positioning method that involves a base station and one or more rovers. The receivers operate in two distinct modes, fix or float. When a receiver is in float mode, the number of integer wavelengths in the carrier-phase measurements has not been resolved yet. In fixed mode, these have been resolved. This is also known as ambiguity resolution. The accuracy is greatly improved if ambiguities are resolved to their correct integer values. During dynamic cases (and even sometimes during static cases), the receiver may change between the two modes repeatedly.
RTK for UAS. RTK positioning can be very useful for UAS, as it can provide a better accuracy in a lot of cases compared to traditional positioning. It can be used for navigational purposes, or for positioning of scientific payloads carried on board a UAS.
RTK use on UAS is currently limited, in part due to the number of challenges associated with it. These include the size and weight issue for smaller UAS. Space is limited on board smaller UAS, and the available payload is also limited. RTK systems require more equipment than a regular GNSS system and therefore require more space and weight.
There is also the issue of cost for smaller UAS. To get quick, high-precision RTK positioning, a dual-frequency receiver is desirable, but such a system is often expensive and could deny a wide sector of the market access to such receivers. Researchers have performed some experiments with an L1-only RTK receiver and show that it could be possible to use such a system for UAS.
The experiments to be discussed in this article assume that the receivers being tested are candidates for possible UAS use. The high-performance GPS single-frequency-receiver evaluation kit used in the RFI tests is considered the prime candidate, as it is a common receiver found on UAS and is relatively cheap and lightweight.
As shown in the previous RFI section, it is possible for RFI to be present and for it to lower the C/N0 without affecting the number of satellites tracked. This could lead to the user being initially unaware of the RFI, and could potentially be a problem for RTK positioning as carrier-phase measurements are more easily disrupted.
Dynamic RTK Experiment. We performed an experiment to evaluate the performance of RTK in a real-world scenario that could be comparable to the use of RTK on a UAS. A comparison between RTK positioning and standard pseudorange-based positioning, essentially the GPS Standard Positioning Service (SPS), was also carried out for one of the receivers. RFI effects were not measured during the experiment.
Almost all post-processing (and some data capturing) was done using RTKLIB, a free and open source GNSS software suite. RTKLIB is modular and can be used at any stage in GNSS applications. The software is available at rtklib.com.
Three receivers were compared: the previously discussed high-performance GPS single-frequency-receiver evaluation kit; a low-cost, high-performance GPS receiver with RTK functionality; and a professional-grade multi-GNSS multi-frequency RTK survey receiver. As the low-cost receiver is marketed for UAS use, it was of interest to see how the receiver compared to the others in a dynamic case. The evaluation-kit receiver was of interest due to similar receivers often being used on UAS today. The professional-grade receiver was of interest since it is a high-end receiver capable of receiving multiple constellations and frequencies. The experiment was performed to simulate some of the conditions that might be experienced on UAS. The most approximate test vehicle that was available at the time was a car.
The receivers were set up to capture GPS signals only. The low-cost and evaluation-kit receivers are only capable of receiving the L1 signal, and were set up accordingly. The professional-grade receiver was set up to capture the L1, L2 and L5 signals. A truth reference for the test vehicle was needed for comparison, and for this we used a multi-frequency receiver with an inertial measurement unit (IMU). The benefit of the IMU is that it contains gyros and accelerometers that can capture very precise movements at times when GNSS signals might not be available (during periods of sky blockage for example).
However, due to the gyros drifting, the IMU needs to be updated with GNSS data every few minutes to give an accurate solution. The receiver was configured to capture GPS L1+L2+L5, GLONASS L1+L2 and WAAS. The GNSS data was then post-processed in precise point positioning (PPP) mode with data from several nearby stations. The GNSS PPP data was then smoothed and combined with the IMU data to form a GNSS PPP plus IMU solution. It was assumed that the GNSS receiver and IMU gave a correct solution at all times. A diagram of the setup can be seen in FIGURE 7.
FIGURE 7. Diagram of the setup of dynamic RTK experiment.
The car with the equipment was driven around the town and campus at the University of Colorado in Boulder. The path included a parking lot (a wide open area), parts of a highway (an open area), major roads (open area with parts covered by trees), residential areas (with many trees covering the sky) and a parking garage (with complete sky blockage). The parking garage was entered towards the end of the experiment.
The receiver data was post-processed using an RTKLIB setup to process the data as if it was received in real time. A multi-frequency multi-GNSS receiver was set up with a roof-mounted antenna at the University of Colorado to collect data for the duration of the experiment, and this data was later used as base-station data for the RTK calculations.
The low-cost receiver had a hard time regaining a position solution, while the evaluation-kit receiver did slightly better. The professional-grade receiver only lost a clear position for about 10 seconds. This behavior agrees with expectations: the low-cost receiver is new and is being updated regularly with new software, and the evaluation-kit receiver is known for being able to perform well under poor conditions. The professional-grade receiver has the support of additional GPS signals, which could explain why it was the first to regain a good position solution.
TABLE 2 shows some of the values calculated from the experiment, which further confirms that the evaluation-kit receiver is able to calculate a position more often than the professional-grade receiver, but a more inaccurate position. In the table, “availability” is defined as how many data points the receiver was able to capture, divided by how many would have been captured if the receiver could capture data at all times. “RTK solution” is how often the captured data was sufficient to calculate an RTK solution. “Fix solution” is defined as how often the ambiguities could be resolved out of the available RTK data points, and “float solution” is how often the ambiguities could not be resolved out the available RTK data points. The comparison of the results using SPS versus the RTK technique for the evaluation-kit receiver is interesting. Using RTK increases the accuracy only slightly, but not as much as anticipated before the test was performed.
Table 2. Tabulated results from the dynamic RTK experiment (N/A = not applicable).
Conclusions
GNSS is viable for UAS navigation, but it remains to be seen how policymakers will decide to regulate its use for this application. Many existing and emerging technologies could prove useful in increasing not only the reliability, but also the accuracy, of the GNSS engine on board a UAS.
Although UAS share many similarities with traditional manned aircraft, by their nature they are unmanned and would not pose the same immediate risk for significant loss of life if an accident were to occur. This, coupled with the fact that UAS can vary greatly in size and operational requirements, leaves the possibility open to using different certification requirements of GNSS navigation for different UAS.
RFI. The RFI experiment showed a considerable impact on C/N0 from the evaluation-kit receiver. While the number of satellites tracked remained constant between tests, it is possible that during slightly different operating conditions (different UAS and/or receivers, other onboard equipment and so on), the impact could have been more severe.
RTK for UAS. RTK systems are complex, but they have some clear advantages to traditional pseudorange-based standalone GNSS, with regard to accuracy. From the results of using the evaluation-kit receiver during the dynamic RTK experiment, it seems as though it would be only advantageous if RTK could be used on a UAS. The only visible difference between the SPS and RTK operation in the experiment was a slight increase in accuracy. The availability of the measurements (that is, how much data was available) was the same for when the receiver used SPS versus RTK. However, the slight increase in accuracy might not be sufficient to compel operators to use the RTK technique for UAS navigation, as additional equipment and setup will be required.
However, when using a receiver with more frequencies, such as the professional-grade receiver, we saw a great increase in accuracy. This receiver was quite large and heavy, and is most likely outside the budget considerations for many smaller UAS setups. It is also likely that using a dual-frequency receiver that is similar to the evaluation-kit receiver in size and weight could improve accuracy, and this should be tested in the future.
Further investigations should be performed to determine if the RTK technique could be used successfully for UAS navigation. A natural next step would be to place an RTK setup on an actual UAS and to test how RFI affects the RTK measurements.
Acknowledgments
This article is based on the paper “GNSS/GPS Robustness for UAS” presented at The Institute of Navigation 2016 International Technical Meeting. The research was carried out in cooperation with the Research and Engineering Center for Unmanned Vehicles in the Department of Aerospace Engineering Sciences at the University of Colorado, Boulder.
JOSHUA STUBBS has an M.Sc. in space engineering, with a focus on aerospace, from Luleå University of Technology in Sweden. In 2015, he did his master’s thesis work at the University of Colorado, Boulder, where he focused on GNSS applications for UAS.
DENNIS M. AKOS completed his Ph.D. degree in electrical engineering at Ohio University, Athens, Ohio, within the Avionics Engineering Center. He is a faculty member with the Aerospace Engineering Sciences Department at the University of Colorado and maintains visiting appointments at Stanford University and Luleå University of Technology.
Further Reading
Authors’ Conference Paper
“GNSS/GPS Robustness for UAS” by J. Stubbs and D. Akos in Proceedings of ITM 2016, the 2016 International Technical Meeting of The Institute of Navigation, Monterey, Calif., Jan. 25–28, 2016, pp. 485–493.
“Radio Frequency Devices” in Code of Federal Regulations, Title 47 (Telecommunication), Chapter I (Federal Communications Commission), Subchapter A (General), Part 15, U.S. National Archives and Records Administration, Washington, DC, 2016.
“Interference, Multipath, and Scintillation” by P.W. Ward, J.W. Betz and C.J. Hegarty, Chapter 6 in Understanding GPS: Principles and Applications, 2nd ed., E.D. Kaplan and C.J. Hegarty, Eds., Artech House, Boston and London, 2006.
“Analytical Derivation of Maximum Tolerable In-Band Interference Levels for Aviation Applications of GNSS” by C.J. Hegarty in Navigation, Vol. 44, No. 1, Spring 1997, pp. 25–34, doi: 10.1002/j.2161-4296.1997.tb01936.x.
“GPS Integrity and Potential Impact on Aviation Safety” by W.Y. Ochieng, K. Sauer, D. Walsh, G. Brodin, S. Griffin and M. Denney in the Journal of Navigation, Vol. 56, No. 1, Jan. 2003, pp. 51–65, doi: 10.1017/S0373463302002096.
Real-Time Kinematic Positioning for Unmanned Aircraft Systems
“A Precise, Low-Cost RTK GNSS System for UAV Applications” by W. Stempfhuber and M. Buchholz in the Proceedings of UAV-g 2011, the 2011 Conference on Unmanned Aerial Vehicles in Geomatics, Zurich, Switzerland, Sept. 14–16, 2011, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII 1/C22, pp. 289–293, 2011.
Aerial refueling requires highly precise relative navigation. (ILLUSTRATION: Charles Park)
Future UAVs will require relative navigation capability to fulfill a broad range of assisted manned and unmanned missions. A new approach, demonstrated in application to aerial refueling, provides access to accurate relative time-space positioning information (R-TSPI) between platforms.
By Shahram Moafipoor, Jeffrey A. Fayman, Lydia Bock and David Honcik
The advent of unmanned aerial vehicles (UAVs) highlights the importance of precise relative navigation information for safe use of UAVs in many application areas. Future military and civilian UAV applications will increasingly require capabilities such as
sense and avoid
swarming
vehicle-to-vehicle (V2V) platooning
docking
autonomous landing and
autonomous aerial-refueling,
all of which require access to accurate relative time-space positioning information (R-TSPI) between platforms.
In this article, we present the foundation for a generic approach to relative navigation capable of meeting the full range of relative assisted manned and unmanned operations. We present a relative extended Kalman filter (R-EKF) that integrates line-of-sight relative observations from GPS as well as non GPS-based onboard sensors measuring relative bearing and/or relative distance. Multi-sensor fusion provides enhanced system integrity and robustness to partial or total lack of GPS-satellite navigation (GPS-denied). The relative navigation system described here uses these technologies, providing up to 100 Hz R-TSPI with an accuracy of up to ±1.0 m (a function of relative distance), ±0.1 m/s velocity and ±0.5º attitude. The system can be applied to a variety of relative navigation applications; here we focus on its use in aerial refueling.
132d Air Refueling Squadron. A Boeing KC-135R Stratotanker refuels an F-22A Raptor. (Photo: USAF)
AERIAL REFUEL CHALLENGES
Automated aerial refueling for manned and unmanned platforms is a challenging problem requiring accurate R-TSPI. The Geo-RelNAV system provides a key measurement for aerial refueling: the vector closure rate, the differential velocity between the tanker and refueling aircraft. The closure rate is monitored in real time onboard the tanker. The measurement can be used to:
maintain safety-of-flight by ensuring refueling aircraft do not exceed a certain velocity,
determine whether or not a refueling aircraft is approaching the tanker with sufficient velocity, and
provide data to drogue-control engineers to improve control law design.
As a GPS/INS system, Geo-RelNAV can produce a relative navigation solution at a faster sample rate than GPS alone. Solutions are available via serial and/or Ethernet (both TCP and UDP) providing input to external systems as well as the tools for analysis engineers to monitor the data in real time using standard monitoring and recording tools. The system provides R-TSPI in different frames, including the body frame of the platforms, local navigation frame (wander-azimuth) and Earth-fixed frame, as well as transferring the solution to arbitrary points of interest on the aircraft such as the refueling aircraft’s refueling probe.
RELATIVE INERTIAL NAVIGATION
We use the terms primary and secondary in this article to identify the platforms for which R-TSPI data is being generated. R-TSPI is always provided for the primary with respect to the secondary. Referring to Figure 1, the tanker is considered the primary and the refueling aircraft, the secondary (or vice versa, depending on the location of the control segment). Data is always transmitted through the data link from the secondary to the primary. Figure 1 summarizes the geometric relations, where the primary body frame is labeled p-frame and the secondary body frame is labeled s-frame. The body frame fixed to the primary (P) is shown by (xPp,yPp,zPp), and body frame fixed to the secondary (S) is shown by (xSs,ySs,zSs).
Fgure 1. Primary/secondary geometry and corresponding body frames fixed to the vehicle body.
The relative navigation equation is set up for the state of the secondary with respect to the state of the primary in the center of the body frame of the primary, p-frame:
(1)
where xPp is the primary position vector established in the p-frame, and xSp is the secondary position vector defined in the p-frame. Note that these vectors can also be obtained from the primary/secondary strapdown inertial navigation solutions after transferring to the reference (eccentric) point. Equation (1) represents the fundamental equation, from which the relative navigation equations are derived. Once the relative kinematic model of the position and velocity are established, the next step is to develop the relative attitude kinematic model. The relative attitude, denoted by the quaternion qpS, is used to map vectors in the s-frame to vectors in the p-frame:
(2)
where qp and qs are the quaternion attitudes of the primary and secondary with respect to the i-frame, qp* is the conjugate of qp, and is the quaternion multiplication operator.
Hardware for the relative navigation system.
RELATIVE EXTENDED KALMAN FILTER
To establish the R-EKF, we must derive the relative inertial error equations. The R-EKF has 21 basic states including nine for relative position, δΔxpPS , relative velocity, δΔvpPS , and relative attitude, Ψpps, and 12 to model the primary’s gyro and accelerometer bias (non-constant) and non-linear scale factors. Since the relative distance between the secondary and primary is small compared to the radius of the Earth, the gravity terms are negligible. Thus, in the linearized terms, the relative gravitational terms are ignored. It should be noted that the secondary states are assumed to be known for retrieving the absolute primary TSPI information. Since Equations (1) and (2) can only provide the general dynamic model for a nonlinear state model, all these equations must be linearized using Taylor series about nominal values (neglecting the higher-order terms). After perturbation state equations are established, they should be discretized from a continuous-time to a discrete-time sequence. The final solution to the state equation can be expressed as:
(3)
with:
(4)
FPpS is the Jacobian matrix, and the perturbation elements are all related to the primary:
(5)
RELATIVE GPS MEASUREMENT MODEL
When GPS is available, high-accuracy relative positions are derived from the use of carrier-phase differential GPS, a technique commonly used in static positioning applications such as surveying. However, unlike those applications, in this case the reference receiver is not stationary; it is located on a moving platform (secondary) creating a moving baseline. The relative GPS measurement in our system is provided by epoch-by-epoch (EBE) differential carrier-phase processing, which measures accurate relative position between the secondary and primary systems. The EBE relative position has a typical accuracy better than 3 cm (1-sigma horizontal) and 6 cm (1-sigma vertical). Testing of the relative measurement was conducted using two ground vehicles configured with 10-Hz dual-frequency GPS sensors. The mean difference was less than 5 cm. As a conclusion, the GPS relative mode was shown to provide accurate relative positions between the platforms. Once the relative position is measured, the R-EKF observation model can be established as:
(6)
The (ΔxpPS )GPS term is the relative position measured by using GPS data, and the term (ΔxpPS)INS is the relative position, which is predicted by using the last updated inertial solutions. Note that in order to use this relative observation, the lever-arm vector between the GPS and IMU of both the primary and the secondary must be accurately measured and applied (see Figure 2).
Figure 2. Relative observation model.
Here, the observation model is represented on the condition that the vector of observations has yielded certain values based on an assumed linear relationship to:
(7)
Equations (3) and (7) are the fundamental equations of the R-EKF.
SYSTEM ARCHITECTURE
Relative navigation is computed and provided at one of the units, designated the primary unit. This requires data from the secondary unit to be transferred to the primary unit over a data link. The primary unit uses this transmitted data to calculate its position, velocity and attitude relative to the secondary unit. Figure 3 summarizes the architecture and data-flow. Mathematically, the data from the secondary unit used in the relative calculations are assumed to be errorless.
Figure 3. Geo-RelNAV architecture.
OPERATIONAL ENVIRONMENT
We distinguish the following three relative navigation stages, illustrated in Figure 4, where each phase utilizes a unique processing mode.
Fgure 4. Relative navigation phases.
In the Approach phase, the data link between primary and secondary units is not closed. An autonomous navigation solution for both the primary and secondary units is computed on each platform independently. This information will be later used when the system transitions to the Engagement phase to initialize the R-EKF.
In the Engagement phase, the data link between primary and secondary units is closed, and the R-TSPI solution is computed between the platforms. Sensor observations are transmitted across the data link from the secondary unit to the primary unit. The primary unit implements the R‑EKF to produce the R-TSPI solution.
In the Departure phase, the activity requiring R-TSPI (that is, refueling) is complete, and the secondary platform pulls away from the primary platform. In this phase, we transition from the R-EKF back to the autonomous independent navigation system.
The Approach phase is as important as the Engagement phase in attenuating the initialization error in terms of position, velocity and attitude. To initialize the R-EKF, the autonomous TSPI solution from the secondary unit is transferred to the primary unit, where the initial relative position, velocity and attitude are estimated.
There are three conditions under which this initialization must occur:
upon transition from the Approach phase to the Engagement phase,
when in the Engagement phase and the system experiences a data link dropout, and
when there is a large latency in the data link. If the data link latency is too large, the data arriving at the primary can no longer be used.
VALIDATION TESTING
Several system tests were conducted including static bench testing, dynamic ground vehicle testing and flight testing. We discuss the results for the static and bench testing here.
For static bench testing, the system was set up on two points with a measured fixed displacement. The sensor configuration included dual-frequency GPS receivers, ring laser gyro-based IMUs, and a data link operating in the 900-MHz frequency band.
The results show that relative position held to the fixed offset with a standard deviation of less than 0.1 m in North, East and Up. Relative velocity held to zero with a standard deviation less than 0.01 m/s, and relative attitude was also maintained with the accuracy up to the gyro bias stability of the ring laser gyro IMU (1°/hr for a stationary platform).
The overall performance of the system in static bench test confirms the stability of the hardware and software of the system, when it is not exposed to any dynamics, and the sensors are in close proximity (no data link latency or data dropouts).
Dynamic Drive Test. In a more realistic test to simulate the operational phases described in Figure 4, the drive test followed a scripted path. As shown in Figure 5, the two platforms left Geodetics’ facility and drove separately (simulated Approach) until they met each other at the Fiesta Island test site, where the data link was closed for the Engagement phase. The primary and secondary navigation systems operated independently during the Approach phase.
Figure 5. Drive test ground trajectory of the primary (blue) and secondary (red).
Once the data link was closed at the test site, the R-EKF engaged, using initialization information transmitted from the secondary to the primary platform. To provide a “truth source” for evaluating the performance of the relative navigation solution, both autonomous GPS/IMU systems were fed data from an external reference receiver. Table 1 shows the statistical data analysis in the form of mean and standard deviation for the collected data.
Average RMS of fit in the relative position, velocity and attitude of approximately 1.0 m, 0.1 m/s and 0.3º, respectively, were computed for the entire relative navigation period. In this dynamic test, we encountered frequent data link dropouts, data link latency, as well as GPS outages, causing discontinuity in the R-EKF measurement updates until GPS was reacquired. During these periods, the R-EKF prediction model, updated with the last calibrated IMU data, provided the R-TSPI. This test help confirm that system performance is at the expected levels, even in the presence of real-world data link and GPS problems.
Table 1. Statistical analysis of the R-TSPI solution.
GPS-DENIED OPERATIONS
Over-reliance on GPS has exposed vulnerabilities associated with this technology. For example, GPS is easily jammed and spoofed. While spoofing can be addressed with Selective Availability Anti-Spoofing (SAASM) technology, and advances such as M-code will mitigate other vulnerabilities, systems of the future must be robust to partial or total lack of GPS. Advanced sensor-fusion technologies are necessary to provide capabilities in conjunction with, and in the absence of, GPS.
In the context of aerial refueling, sensors such as active and passive vision systems can be used as complimentary observations by the system, providing a GPS-free relative distance observation in situations where GPS is blocked due to airframe masking, jamming, and so on.
Data from both active (lidar) and passive (camera) vision sensors were added to the system, providing significant advantages in the process flow. The use of vision sensors provides the relative distance observation in GPS-denied conditions for continuity in R-EKF updating. In addition, vision-based relative distance allows for the detection of outliers by evaluating the redundancy contribution of the measured GPS-based relative distance, and enables the transfer of the R-TSPI solution from the secondary refueling center to the on-the-fly probe-drogue system, as shown in Figure 6.
Figure 6. Vision sensor aiding increasing the integrity
For the active vision system, we leveraged a fully integrated lidar mapping payload as shown in Figure 7 (left). For the passive sensor, we utilize a stereo camera. Figure 7 (right) shows the test area and the simulated drogue. Imagery observations from the passive camera and the lidar system were processed with independent algorithms appropriate to each data type and the relative distance between each of the two sensors, and the simulated drogue was measured with an RMS error of less than 10 cm.
Figure 7. Geo-MMS (left) and its application (right) for measuring relative distance.
INTEGRITY
While outside the scope of this article, in addition to supplying a GPS-free relative distance observation, the use of vision sensors was applied to the task of increasing system integrity. This includes, in general, the capability to indicate when the system should not be used for the intended operation. We focused on two aspects: outlier detection (inner reliability), and the effect of undetected outliers (outer reliability).
To properly address the reliability and integrity requirements, a quality testing mechanism was designed to assess the estimated/predicted relative distance observations before passing them in to the R-EKF module.
CONCLUSIONS
An autonomous relative navigation, in its application for the aerial refueling problem, places special attention on system architecture so that it can handle most possible real-world scenarios, including frequent data link dropouts, data link latency and GPS outages. The core of the system is a relative extended Kalman filter, which uses GPS and IMU measurements of the primary and secondary platforms to estimate the relative inertial navigation states. The system is able to provide relative TSPI at the IMU sample rate with an accuracy of ±1.0 m position, 0.1 m/s velocity and ±0.5º attitude.
An added benefit of the system architecture is the ability to add observation models that do not rely on GPS. Thus, redundancy can be introduced using sensors such as vision systems.
SHAHRAM MOAFIPOOR is a senior navigation scientist at Geodetics, focusing on new sensor technologies, sensor-fusion architectures, application software, embedded firmware and sensor interoperability in GPS and GPS-denied environments. He holds a Ph.D. in geodetic science from The Ohio State University.
JEFFREY A. FAYMAN serves as Geodetics’ CTO. He holds a Ph.D. in computer science from the Technion Israel Institute of Technology and has published more than 40 papers in robotics, computer vision, computer graphics and navigation systems.
LYDIA BOCK serves as Geodetics’ president and CEO. She has more than 35 years of industry experience spanning a variety of high-tech industries including electronics, semiconductors and telecommunications. She has a Ph.D. from the Massachusetts Institute of Technology.
DAVID HONCIK, Geodetics’ director of engineering, has more than 30 years of experience in software/hardware integration and structured software design for real-time embedded systems, Windows programs, graphics, telecommunications, aerospace, flight simulation and airborne instrumentation.
The integrated lidar mapping payload referenced is Geodetics’ Geo-MMS system.
Exploiting terrestrial signals of opportunity (SOPs) can significantly reduce the vertical dilution of precision (VDOP) of a GNSS navigation solution. Simulation and experimental results show that adding cellular SOP observables is more effective in reducing VDOP than adding GNSS space vehicle (SV) observables.
By Joshua J. Morales, Joe J. Khalife and Zaher M. Kassas
GNSS position solutions can in many cases suffer from a high vertical dilution of precision (VDOP) due to lack of space vehicle (SV) angle diversity. Signals of opportunity (SOPs) have been recently considered to enable navigation whenever GNSS signals become inaccessible or untrustworthy. Terrestrial SOPs are abundant and are available at varying geometric configurations, making them an attractive supplement to GNSS for reducing VDOP.
Common metrics used to assess the quality of the spatial geometry of GNSS SVs are the parameters of the geometric dilution of precision (GDOP); namely, horizontal dilution of precision (HDOP), time dilution of precision (TDOP), and VDOP. Several methods have been investigated for selecting the best GNSS SV configuration to improve the navigation solution by minimizing the GDOP. While the navigation solution is always improved by additional observables from GNSS SVs, the solution’s VDOP generally remains of lesser quality than the HDOP. GPS augmentation with terrestrial transmitters that transmit GPS-like signals have been shown to reduce VDOP. However, this requires installation of additional proprietary infrastructure.
This article studies VDOP reduction by exploiting terrestrial SOPs, particularly cellular code division multiple access (CDMA) signals, which have inherently low elevation angles and are free to use.
In GNSS-based navigation, the states of the SVs are readily available. For SOPs, however, even though the position states may be known a priori, the clock-error states are dynamic; hence, they must be continuously estimated. The states of SOPs can be made available through one or more receivers in the navigating receiver’s vicinity. Here, the estimates of such SOPs are exploited and the VDOP reduction is evaluated.
PROBLEM FORMULATION
Consider an environment comprising a receiver, M GNSS SVs, and N terrestrial SOPs. Each SOP will be assumed to emanate from a spatially stationary transmitter, and its state vector, xsop(n), will consist of its three-dimensional (3-D) position rsop(n) and clock bias cδtsop(n), where n=1,…,N and c is the speed of light. The receiver draws pseudorange observations from the GNSS SVs and from the SOPs. The observations are fused through an estimator whose role is to estimate the state vector of the receiver xr=[rrT, cδtr] T, where rr and cδtr are the 3D position and clock bias of the receiver, respectively. To simplify the discussion, assume that the pseudorange observation noise is independent and identically distributed across all channels with variance σ2. The estimator produces an estimate of the receiver’s state vector and associated estimation error covariance P =σ2(HTH)-1.
Without loss of generality, assume an East-North-Up (ENU) coordinate frame to be centered at . In this frame, the dilution of precision matrix G≡(HTH)-1 is completely determined by the azimuth and elevation angles from the receiver to each SV, denoted azsv(m) and elsv(m), respectively, and the receiver to each SOP, denoted azsop(n) and elsop(n), respectively, where m=1,…,M. Hence, the quality of the estimate depends on these angles and the pseudorange observation noise variance σ2. The diagonal elements of G, denoted gii, are the parameters of the dilution of precision (DOP) factors:
Therefore, the DOP values are directly related to the estimation error covariance; hence, the more favorable the azimuth and elevation angles, the lower the DOP values. If the observation noise was not independent and identically distributed, the weighted DOP factors must be used.
VDOP REDUCTION VIA SOPs
With the exception of GNSS receivers mounted on high-flying and space vehicles, all GNSS SVs are typically above the receiver, that is, the receiver-to-SV elevation angles are theoretically limited between 0°≤elsv(m)≤90°. GNSS receivers typically restrict the lowest elevation angle to some elevation mask, elsv,min, so to ignore GNSS SV signals that are heavily degraded due to the ionosphere, troposphere and multipath.
As a consequence, GNSS SV observables lack elevation angle diversity, and the VDOP of a GNSS-based navigation solution is degraded. For ground vehicles, elsv,min is typically between 5° and 20°. These elevation angle masks also apply to low-flying aircraft, such as small unmanned aerial vehicles (UAVs), whose flight altitudes are limited to 500 feet (approximately 152 meters) by the Federal Aviation Administration (FAA).
In GNSS + SOP-based navigation, the elevation angle span may effectively double, specifically –90°≤elsop(n)≤90°. For ground vehicles, useful observations can be made on terrestrial SOPs that reside at elevation angles of elsop(n)=0°. For aerial vehicles, terrestrial SOPs can reside at elevation angles as low as elsop(n)=–90°, for example, if the vehicle is flying directly above the SOP transmitter.
To illustrate the VDOP reduction by incorporating additional GNSS SV observations versus additional SOP observations, an additional observation at elnew is introduced, and the resulting VDOP(elnew) is evaluated. To this end, M SV azimuth and elevation angles were computed using GPS ephemeris files accessed from the Yucaipa, California, station from Garner GPS Archive, which are tabulated in Table 1.
Table 1. SV azimuth and elevation angle (degrees).
For each set of GPS SVs, the azimuth angle of an additional observation was chosen as a random sample from a uniform distribution between 0° and 360°, that is, aznew~U(0°,360°). The corresponding VDOP for introducing an additional measurement at a sweeping elevation angle –90°≤elnew≤90° are plotted in Figure 1 (a)–(d) for M=4,…,7, respectively.
Figure 1. A receiver has access to M GPS SVs from Table 1. Plots (a)- (d) show the VDOP for each GPS SV configuration before adding an additional measurement (red dotted line) and the resulting VDOP(elnew) for adding an additional measurement (blue curve) at an elevation angle –90°≤elnew≤90° for M=4,…,7, respectively.
The following can be concluded from these plots. First, while the VDOP is always improved by introducing an additional measurement, the improvement of adding an SOP measurement is much more significant than adding an additional GPS SV measurement. Second, for elevation angles inherent only to terrestrial SOPs, that is, –90°≤elsop(n)≤0°, the VDOP is monotonically decreasing for decreasing elevation angles.
SIMULATION RESULTS
To compare the VDOP of a GNSS-only navigation solution with a GNSS + SOP navigation solution, simulations were conducted using receivers mounted on ground and aerial vehicles.
Ground Receiver. The position of a receiver mounted on a ground vehicle was set to rr ≡(106 )•[– 2.431171,– 4.696750, 3.553778]T expressed in an Earth-Centered-Earth-Fixed (ECEF) coordinate frame. The elevation and azimuth angles of the GPS SV constellation above the receiver over a 24-hour period was computed using GPS SV ephemeris files from the Garner GPS Archive. The elevation mask was set to elsv,min≡20°. The azimuth and elevation angles of three SOPs, which were calculated from surveyed terrestrial cellular CDMA tower positions in the navigating receiver’s vicinity, were set to azsop≡[42.4°,113.4°,230.3° ]T and elsop ≡[3.53°,1.98°,0.95°]T, respectively. The resulting VDOP, HDOP, GDOP and associated number of available GPS SVs for a 24-hour period starting from midnight, Sept. 1, 2015, are plotted in Figure 2.
Figure 2. Fig. (a) represents the number of SVs with an elevation angle >20° as a function of time. Fig. (b)-(d) correspond to the resulting VDOP, HDOP, and GDOP, respectively, of the navigation solution using GPS only, GPS + 1 SOP, GPS + 2 SOPs, and GPS + 3 SOPs.
The following can be concluded from these plots. First, the resulting VDOP using GPS + N SOPs for N≥1 is always less than the resulting VDOP using GPS alone. Second, using GPS + N SOPs for N≥1 prevents large spikes in VDOP when the number of GPS SVs drops. Third, using GPS + N SOPs for N≥1 also reduces both HDOP and GDOP.
Unmanned Aerial Vehicle. The initial position of a receiver mounted on a UAV was set to rr ≡(106 )•[–2.504728, –4.65991, 3.551203]T. The receiver’s true trajectory evolved according to velocity random walk dynamics. Pseudorange observations on all available GPS SVs above an elevation mask set to elsv,min≡20° and three terrestrial SOPs were generated using a MATLAB-based simulator. The simulator used SV trajectories which were computed using GPS SV ephemeris files from Sept. 1, 2015, 10:00 to 10:03 a.m.
The positions of the SOPs were set to rsop(1)≡(106)•[– 2.504953,– 4.659550, 3.551292]T, rsop(2)≡(106)•[– 2.503655, –4.659645, 3.552050]T, and rsop(3)≡(106)•[– 2.504124,– 4.660430, 3.550646]T, which are the locations of surveyed cellular towers in the UAV’s vicinity. The UAV’s true trajectory, navigation solution from using only GPS SV pseudoranges, and navigation solution from using GPS and SOP pseudoranges are illustrated in Figure 3 (top). The corresponding 95th-percentile uncertainty ellipsoids for a sample set of navigation solutions are illustrated in Figure 3 (bottom).
Figure 3 . Simulation results for a UAV flying over downtown Los Angeles. Top: Illustration of the true trajectory (red curve), navigation solution from using pseudoranges from six GPS SVs (yellow curve), and navigation solution from using pseudoranges from six GPS SVs and three cellular CDMA SOPs (blue curve). Bottom: Illustration of uncertainty ellipsoid (yellow) of GPS only navigation solution and uncertainty ellipsoid (blue) of GPS + SOP navigation solution.
The following can be noted from these plots. First, the accuracy of the vertical component of the GPS-only navigation solution is worse than that of the GPS + SOP navigation solution. Second, the uncertainty in the vertical component of the GPS-only navigation solution is larger than that of the GPS + SOP navigation solution, which is captured by the yellow and blue uncertainty ellipsoids, respectively. Third, the accuracy of the horizontal component of the navigation solution is also improved by incorporating cellular SOP pseudorange observations alongside GPS SV pseudorange observations.
EXPERIMENTAL RESULTS
A field experiment was conducted using software-defined receivers (SDRs) to demonstrate the reduction of VDOP obtained from including SOP pseudoranges alongside GPS pseudoranges for estimating the states of a receiver. To this end, two antennas were mounted on a vehicle to acquire and track multiple GPS signals and three cellular base transceiver stations (BTSs) whose signals were modulated through CDMA. The GPS and cellular signals were simultaneously downmixed and synchronously sampled via two universal software radio peripherals (USRPs). These front-ends fed their data to the Multichannel Adaptive TRansceiver Information eXtractor (MATRIX) SDR, developed at the Autonomous Systems Perception, Intelligence and Navigation (ASPIN) Laboratory at the University of California, Riverside. The LabVIEW-based MATRIX SDR produced pseudorange observables from five GPS L1 C/A signals in view and the three cellular BTSs.
Figure 4 depicts the experimental hardware setup.
Figure 4. Experiment hardware setup.
The pseudoranges were drawn from a receiver located at rr≡(106)•[– 2.430701,– 4.697498, 3.553099]T, expressed in an ECEF frame, which was surveyed using a carrier-phase differential GPS receiver. The corresponding SOP state estimates were collaboratively estimated by receivers in the navigating receiver’s vicinity. The pseudoranges and SOP estimates were fed to a least-squares estimator, producing x^r and associated P from which the VDOP, HDOP, and GDOP were calculated and tabulated in Table 2 for M GPS SVs and N cellular CDMA SOPs. A sky plot of the GPS SVs used is shown in Figure 5.
Figure 5. Left: Sky plot of GPS SVs: 14, 21, 22, and 27 used for the four SV scenarios. Right: Sky plot of GPS SVs: 14, 18, 21, 22, and 27 used for the five SV scenarios. The elevation mask, elsv,min, was set to 20° (dashed circle).
The tower locations, receiver location and a comparison of the resulting 95th-percentile estimation uncertainty ellipsoids of for {M,N}={5,0} and {5,3} are illustrated in Figure 6.
Figure 6. Top: Cellular CDMA SOP tower locations and receiver location. Bottom: Uncertainty ellipsoid (yellow) of navigation solution from using pseudoranges from five GPS SVs and uncertainty ellipsoid (blue) of navigation solution from using pseudoranges from five GPS SVs and three cellular CDMA SOPs.
The corresponding vertical error was 1.82 meters and 0.65 meters respectively. Hence, adding three SOPs to the navigation solution that used five GPS SVs reduced the vertical error by 64.3 percent. Although this is a significant improvement over using GPS observables alone, improvements for aerial vehicles are expected to be even more significant, since they can exploit a full span of observable elevation angles as demonstrated in the simulation section.
Table 2. DOP values for M SVs + N SOPs.
CONCLUSION
This article studied the VDOP reduction of a GNSS-based navigation solution by exploiting terrestrial SOPs. It was demonstrated that the VDOP of a GNSS solution can be reduced by exploiting the inherently small elevation angles of terrestrial SOPs. Experimental results using ground vehicles equipped with SDRs demonstrated VDOP reduction of a GNSS navigation solution by exploiting a varying number of cellular CDMA SOPs. Incorporating terrestrial SOP observables alongside GNSS SV observables for VDOP reduction is particularly attractive for aerial systems, since a full span of observable elevation angles becomes available.
MANUFACTURERS
Two National Instruments universal software radio peripherals were used in the experiment. A Trimble 5700 receiver surveyed the experimental receiver location.
JOSHUA J. MORALES is pursuing a Ph.D. in electrical and computer engineering at the University of California, Riverside.
JOE J. KHALIFEH is a Ph.D. student at the University of California, Riverside.
ZAHER (ZAK) M. KASSAS is an assistant professor at the University of California, Riverside. He received a Ph.D. in electrical and computer engineering from the University of Texas at Austin. Previously, he was a research and development engineer with the LabVIEW Control Design and Dynamical Systems Simulation Group at National Instruments Corp.
This article is based on a technical paper presented at the 2016 ION ITM conference in Monterey, California.
