The United States Federal Aviation Administration (FAA) and its government partners are expanding research on ways to detect “rogue” drones around airports. Together, they are evaluating drone detection technology at John F. Kennedy International Airport (JFK) in New York.
Over the last two years, the FAA has received numerous reports from pilots and residents about unmanned aircraft systems — UAS, or “drones” — around some of the nation’s busiest airports, including JFK.
“We face many difficult challenges as we integrate rapidly evolving UAS technology into our complex and highly regulated airspace,” said Marke “Hoot” Gibson, FAA senior advisor on UAS integration. “This effort at JFK reflects everyone’s commitment to safety.”
Terminal 6 at JFK Airport. (Photo: New York Photo Gallery)
Beginning May 2, the FAA conducted evaluations at JFK to study the effectiveness of a Federal Bureau of Investigation (FBI) UAS detection system in a commercial airport environment. Five different rotorcraft and fixed-wing UAS participated in the evaluations, and about 40 separate tests took place.
The JFK evaluation involved extensive government inter-agency collaboration, and cooperation from industry and academia. The tests expanded on research performed earlier this year at Atlantic City International Airport.
In addition to the FAA and the FBI, the agencies combining forces in this research included the Department of Homeland Security (DHS), Department of Justice, Queens District Attorney’s Office and the Port Authority of New York and New Jersey. DHS and the FBI want to identify unauthorized UAS operators for law enforcement purposes, and the FAA’s mission is to provide a safe and efficient airport environment for both manned and unmanned air traffic.
“We applaud the FBI and FAA for their efforts to detect and track unmanned aerial systems (UAS),” said Thomas Bosco, Port Authority aviation director. “We look forward to supporting continued U.S. government efforts to identify and deploy countermeasures to neutralize the threat posed by rogue UASs.”
The team evaluating the FBI’s detection system also included contributions from one of the six FAA-designated UAS test sites. The Griffiss International Airport test site in Rome, New York, provided expertise in planning the individual tests as well as the flight commander for the tests and two of the UAS used.
The FY 2016 Appropriations law mandates that the FAA continue research into detection of UAS in airport environments. The agency is continuing to formulate an inter-agency strategy to evaluate detection systems in a variety of airport environments.
In the most critical phase of the landing maneuver, the UAV flight control system must compensate for the accelerated air flow above the ground vehicle. (Photo: DLR)
Moving at 75 kilometers an hour (47 mph) an unmanned, electric, autonomous aircraft settled gently on the roof of a moving car.
Scientists from the German Aerospace Center (DLR) Institute of Robotics and Mechatronics combined robotics and unmanned aerial vehicles (UAVs) to develop a system where a fixed-wing aircraft automatically lands on a moving ground vehicle.
The DLR system is designed for commercial applications such as remote sensing and communication. It could be applied to ultra-lightweight solar aircraft that complement traditional satellite systems in the stratosphere. Or, it could support crisis management, such as aiding disaster-communications networks or providing data on climate change.
Losing weight
Ultralight solar aircraft can reach more than 20 kilometers in altitude. The weight factor is crucial to how long the ultralight can stay in the air.
The Demonstrator Platform Penguin BE UAV is equipped with redundnant landing hardware. (Photo: DLR)
By omitting the traditional landing gear, the dead weight of these UAVs can be significantly reduced. This allows more load capacity, greater range and better performance. A lighter craft also increases payload capacity, creating more space for scientific instruments.
In flight tests on an airfield in Swabia Mindelheim-Mattsies, the DLR system was successfully tested with a 3-meter, 20-kilogram, electric fixed-wing UAV. A net was provided on the roof of a car, along with optical markers. The UAV can position itself up to half a meter over the 4 x 5 meter landing platform. The optical multi-marker tracking system detects the landing apparatus and determines the relative position of the ground vehicle with high accuracy. The computer-controlled landing is then carried out.
Movement of UAV and the vehicle are adjusted with the help of special algorithms. With the car and the UAV moving at the same speed, the landing is more like a settling, making the landing safer and easier. Though designed for both autonomous car and UAV, a driver remained in the car for safety during the tests. A robotic vehicle without a driver will be tested next.
The work was supported by the EU project EC-Safe Mobile Support and complement the activities of the Flight Robotics Group.
In the semi-autonomous landing vehicle, the driver receives control commands via a graphical display. The crosshairs indicate the location of the UAV. (Photo: DLR)
Insitu and BNSF officials launch ScanEagle in support of the FAA’s pathfinder initiative (Photo: Insitu)
Insitu and PrecisionHawk have formed a strategic alliance to provide UAS solutions that help commercial enterprises achieve safe unmanned flight for extended and beyond-visual-line-of-sight operations. Insitu is a provider of information and unmanned aircraft systems (UAS) for commercial, civil and military operations, and PrecisionHawk is an aerial data provider.
Both companies are exhibiting at this week’s AUVSI Xponential 2016 show in New Orleans.
The alliance also leverages the extensive research and testing capabilities of two of the participants of the Federal Aviation Administration (FAA) Pathfinder Program, which is dedicated to expanding the use of UAS within the nation’s airspace.
“While our businesses are diverse, the areas where we intersect have tremendous potential for creating new opportunities in the commercial industries we both serve,” said Ryan M. Hartman, Insitu President and CEO. “This alliance ensures that more businesses will explore what unmanned technology can offer.”
Thanks to the integration of each company’s proprietary platforms, hardware and software, Insitu and PrecisionHawk plan to deliver even more data insights.
“Our customers are always pushing us to bring more advanced and comprehensive solutions, and we go above and beyond to make sure we are developing tools that serve their specific needs,” said PrecisionHawk president Christopher Dean. “We believe this alliance with Insitu will help us deliver on our promise even more.”
The emphasis of the U.S.-based alliance is on providing business intelligence support for commercial operations, including asset protection, property preservation, safety enhancement and environmental monitoring.
Speaking today at Xponential, the AUVSI annual conference in New Orleans, FAA Administrator Michael Huerta announced the agency is establishing a broad-based advisory committee that will provide advice on key unmanned aircraft integration issues. He also announced plans to make it easier for students to fly unmanned aircraft as part of their coursework.
Huerta said the drone advisory committee is an outgrowth of the successful stakeholder-based UAS registration task force and the MicroUAS aviation rule-making committee.
Those panels were set up for a single purpose and for limited duration. In contrast, the drone advisory committee is intended to be a long-lasting group. It will help identify and prioritize integration challenges and improvements, and create broad support for an overall integration strategy.
“Input from stakeholders is critical to our ability to achieve that perfect balance between integration and safety,” Huerta said. “We know that our policies and overall regulation of this segment of aviation will be more successful if we have the backing of a strong, diverse coalition.”
Huerta said he has asked Intel CEO Brian Krzanich to chair the group.
Student UAS operation
Huerta also announced the FAA will start allowing students to operate UAS for educational and research purposes today.
As a result, schools and students will no longer need a Section 333 exemption or any other authorization to fly provided they follow the rules for model aircraft. Faculty will be able to use drones in connection with helping their students with their courses.
“Schools and universities are incubators for tomorrow’s great ideas, and we think this is going to be a significant shot in the arm for innovation,” Huerta said.
SOAR Oregon, a non-profit organization focused on the development of the unmanned aircraft systems (UAS) industry in Oregon, has given the city of Pendleton a grant for the establishment of a FutureFarm project at the Pendleton UAS Test Range.
The Oregon UAS FutureFarm is a real-world proving ground designed to help digital agriculture pioneers accelerate product development, reduce cycles and expand market growth.
SOAR Oregon is exhibiting at AUVSI Xponential 2016, being held in New Orleans this week.
Pendleton Mayor Phil Houk (right) signs the FutureFarm grant agreement with SOAR Oregon. SOAR Oregon’s John Stevens (front left), Roundup City Development Corporation’s Mike Short (back left), and Pendleton UAS Range’s Steve Chrisman (back right) were on hand to witness the signing.
Once established in June, it will be the only digital agriculture proving ground of its caliber in the United States, SOAR Oregon said. Developers of agriculture-focused unmanned robotics and data systems will find the Oregon UAS FutureFarm has a broad spectrum of high value and commodity crops, multiple layers of remote sensing for benchmarking, and access to the agricultural knowledge base they need to test, validate and innovate the next generation of interconnected unmanned and automated agricultural systems.
The Oregon UAS FutureFarm features a network of research-friendly farmers growing a large variety of irrigated and dry-land crops in both traditional and modern farming infrastructures. Strategic partners include the City of Pendleton, Digital Harvest, SOAR Oregon, Blue Mountain Community College, Oregon State University and USDA Columbia Basin Agricultural Research Center.
“We believe that the Oregon UAS FutureFarm fills a clearly defined market niche for UAS platform and payload developers who are working on the next generation of technologies for precision agriculture,” said SOAR Oregon Executive Director Chuck Allen. “We are especially pleased that this project is taking place at one of Oregon’s FAA-designated UAS test ranges.”
“We are pleased to be supporting the Oregon UAS FutureFarm as both a partner and user,” said Young Kim, CEO of Digital Harvest. “The fact that the test range includes high-value tree fruit orchards, premium wine grape vineyards, hundreds of automated irrigated plots, and hundreds of thousands of acres of dry land farms makes it a unique and special zone.”
“The Oregon UAS FutureFarm is open to UAS developers, sensor makers, robotics companies, universities and any others who are looking for a real-world digital agriculture proving ground that is supported by a collaborative innovation focused community,” said Jeff Lorton, Oregon UAS FutureFarm project manager.
Pendleton Mayor Phil Houk signed the agreement with John Stevens and Mike Short from SOAR in attendance. “The FutureFarm represents what we’d hoped the Pendleton UAS Range could become — not just an environment for the development of technology, but the place where real-world questions could be solved with unmanned aircraft,” said Steve Chrisman, Pendleton director of Economic Development. “We are excited about the potential of this project to develop solutions which benefit growers across the Northwest.”
IMSAR LLC, manufacturer of miniaturized synthetic aperture radar (SAR), is selling its detect and avoid radar technology to Fortem Technologies. The technology powered IMSAR’s previously announced family of collision-avoidance radar designed for the commercial unmanned aerial systems (UAS) market.
The Federal Aviation Administration (FAA) requires an aircraft operating in civil airspace to be able to “see and avoid” other aircraft. Collision-avoidance systems seek to meet this requirement by allowing UASs to detect other airborne objects, predict potential midair collisions, and automatically maneuver the UAS to avoid catastrophes.
A radar-based sense-and-avoid solution for small UAS was previously not viable because of high cost, weight and complex technology and algorithms required. Fortem’s product will enable small UAS to avoid mid-air collisions with manned or unmanned aircraft as well as targets that lack a transponder, such as cranes, paving the way for the integration of UAS into civil airspace worldwide.
“Radar is ideally suited because it operates effectively in darkness, cloud cover, fog, smoke and precipitation,” said Britton Quist, IMSAR’s CTO.
According to Ryan Smith, CEO, IMSAR, key development milestones have been met allowing the spin out of sense and avoid to Fortem Technologies. Adam Robertson, vice president of IMSAR, will be leaving to join Fortem Technologies after nine years at IMSAR.
Fortem Technologies has announced product availability in July 2016.
Fortem and IMSAR products are on display May 2-5 at the Xponential show in New Orleans, Booth 134.
IMSAR LLC, manufacturer of miniaturized synthetic aperture radar (SAR), is selling its detect and avoid radar technology to Fortem Technologies. The technology powered IMSAR’s previously announced family of collision-avoidance radar designed for the commercial unmanned aerial systems (UAS) market.
The Federal Aviation Administration (FAA) requires an aircraft operating in civil airspace to be able to “see and avoid” other aircraft. Collision-avoidance systems seek to meet this requirement by allowing UASs to detect other airborne objects, predict potential midair collisions, and automatically maneuver the UAS to avoid catastrophes.
A radar-based sense-and-avoid solution for small UAS was previously not viable because of high cost, weight and complex technology and algorithms required. Fortem’s product will enable small UAS to avoid mid-air collisions with manned or unmanned aircraft as well as targets that lack a transponder, such as cranes, paving the way for the integration of UAS into civil airspace worldwide.
“Radar is ideally suited because it operates effectively in darkness, cloud cover, fog, smoke and precipitation,” said Britton Quist, IMSAR’s CTO.
According to Ryan Smith, CEO, IMSAR, key development milestones have been met allowing the spin out of sense and avoid to Fortem Technologies. Adam Robertson, vice president of IMSAR, will be leaving to join Fortem Technologies after nine years at IMSAR.
Fortem Technologies has announced product availability in July 2016.
Fortem and IMSAR products are on display May 2-5 at the AUVSI Xponential show in New Orleans, Booth 134.
Quanergy Systems, a provider of lidar sensors and smart sensing solutions, is offering a new sensor.
Quanergy’s S3 lidar sensor
The S3-Qi is a miniature solid-state lidar sensor that is 15 percent the size of the previous solid-state model, the S3. Quanergy is displaying the new sensor along with its other products in Booth 767 at AUVSI’s Xponential May 3-5 in New Orleans.
The S3-Qi, offered four months after the original S3, has a smaller 1 inch by 1.5-inch footprint, weighs about 100 grams and has low power consumption. The small form factor, combined with a cost-effective design, makes the S3-Qi well suited for applications such as drones, intelligent robotics, security, smart homes and industrial automation.
Mass production of the S3-Qi is targeted for the first quarter of 2017.
“We are excited to raise the bar, once again, with the expansion of our product portfolio,” said Louay Eldada, Quanergy CEO. “We continue to push the boundaries on behalf of our customers. The S3-Qi is a testament to our focus on the user and our investment in innovation for game-changing smart sensing solutions offered at price points that make their use ubiquitous. In drones, payload and battery runtime benefit greatly from our compact sensors.”
Quanergy’s lidar sensors have applications in more than 30 market verticals including security, transportation, terrestrial and aerial mapping, and industrial automation.
Echodyne today announced the development of MESA-DAA, an Airborne Detect and Avoid (DAA) radar for small to medium-sized unmanned aircraft systems (UAS).
Echodyne made the announcement at AUVSI’s Xponential 2016 trade show and conference.
The small, lightweight and low power DAA radar will operate at K-band and be capable of rapidly scanning a broad field of view in azimuth and elevation at ranges out to 3 kilometers. MESA-DAA is based on Echodyne’s patented Metamaterials Electronically Scanning Array (MESA), which offers breakthrough cost, size, weight, and power (C-SWAP) improvements over traditional electronically scanning array technology.
The MESA-DAA radar is scheduled for release at the end of 2016 and will be an evolution of the MESA-K-DEV radar, which Echodyne released today.
“Detect and avoid is the single biggest technical hurdle to opening up the National Airspace System to UAS,” said Jim Williams, former head of the Federal Aviation Administration’s (FAA) UAS Integration Office and current Principal at Dentons US, LLP and Echodyne advisor.
“NASA, the FAA, industry and academia have spent years studying the DAA problem and have determined radar is by far the best sensor, if not the only sensor, capable of providing the all-weather, long-range, and broad field of view scanning that is necessary for safe, highly reliable DAA. MESA-DAA technology may well represent the key to safely opening up airspace for beyond visual line of sight operations.”
Detect and Avoid Requirement
One of the FAA’s central aircraft operating rules is that pilots maintain vigilance so as to see and avoid other aircraft. To fulfill this requirement, UAS need to remain within visual line of sight of their pilot.
Although the regulations for UAS are still in development, there is widespread acceptance that for UAS to fly beyond line of sight of their operator, they will need DAA sensors and systems that safely replace the pilot’s see and avoid capability. This DAA capability will need to detect both cooperative objects (those transmitting their position with a transponder) and non-cooperative objects (aircraft without transponders, birds, etc.).
Radar is the only sensor capable of reliably performing DAA in all weather conditions and at the ranges, broad fields of view and scanning speeds necessary for safe operation of UAS in the NAS. Radar is the only sensor that directly measures the position of an object (such as range, azimuth, elevation) as well as its relative speed of approach (via Doppler).
“We believe MESA-DAA will be a critical technology for safely opening up the National Airspace System to small UAS for beyond visual line of sight operations,” said Eben Frankenberg, founder and CEO of Echodyne. “Radar is the sensor of choice for DAA, but existing radar technology is too slow, too bulky and too expensive to provide DAA radar capabilities on small UAS. The C-SWAP characteristics of MESA and our DAA radar are completely unparalleled and uniquely well suited for small UAS.”
In the April 7 “FAA Aerospace Forecast,” the FAA reports that it has already granted more than 4,000 Section 333 Exemptions for commercial UAS operations, clear evidence of the high demand for UAS applications. The FAA forecasts that sales of commercial small UAS could exceed 600,000 for 2016 and grow to 2.7 million by 2020, noting that “the overall demand for commercial UAS will soar once regulations more easily enable beyond visual line of sight operations and operations of multiple unmanned aircraft by a single pilot.”
MESA-DAA Specifications. MESA-DAA is based largely on Echodyne’s existing MESA-K-DEV radar. Package size and weight are expected to be less than MESA-K-DEV, especially if the unit is placed inside the UAS. Range is expected to be 3 kilometers, and scanning speed is expected to be 1 Hz for the entire field of view and as fast as 10 Hz for updating locations on previously detected objects. The field of view for a single unit is expected to be ±60 degrees in azimuth (120 degrees total) and ±45 degrees in elevation. Multiple units can be combined if greater field of view is desired.
MESA-K-DEV. Echodyne also announced availability of MESA-K-DEV, an ultra-low C-SWAP, fast electronically scanning radar based on its patented MESA. The radar operates at K-band. The fully self-contained and packaged unit measures 22 by 7.5 by 2.5 centimeters and weighs 820 grams.
Unlike conventional mechanical apertures that steer a radar beam using motorized gimbals, Echodyne’s MESA requires no moving parts to steer its beam. And unlike phased array radars or active electronically scanning array radars that require complicated and expensive transmit/receive modules — including phase shifters, amplifiers, circulators and low noise amplifiers behind every single antenna element — MESA uses a simpler meta-materials architecture. The net effect of this simplified architecture is lower cost, size, weight and power.
The Federal Aviation Administration (FAA) took a major step forward in expanding commercial UAS/UAV operations in the U.S. airspace. It’s chief said April 19 that the FAA is preparing to take another major step forward in further opening up commercial UAS/UAV operations by eliminating the need for a 333 Exemption for operating small UAS/UAV.
On March 29, the FAA announced it was doubling the altitude for blanket nationwide CoAs (Certificates of Waiver or Authorization) to 400 feet above ground level (AGL). The FAA has typically issued a blanket nationwide CoA with each 333 Exemption it has granted.
Before the announcement, the maximum altitude allowed for commercial operations under the blanket CoA was 200 feet AGL. Now, it is 400 feet AGL. At the stroke of a pen, the 3,000+ 333 Exemption holders with blanket CoAs are now authorized to fly to 400 feet. This is significant because UAS operators can now fly higher and cover more area more efficiently, and still meet the precision and accuracy requirements of most clients.
Another announcement, perhaps even more important, was made by FAA Administrator Michael Huerta, who spoke at the 2016 FAA UAS Symposium held April 19-20 in Daytona Beach, Florida. Huerta announced that the FAA is close to finalizing the FAA rules for small UAS.
“In late spring we plan to finalize our small UAS rule to eliminate the need for most 333 exemptions,” Huerta said. He was referring to the Small UAS Notice of Proposed Rulemaking (NPRM) that was announced Feb. 15, 2015, and opened for public comment through April 24, 2015. There were 4,650 public comments made. You can read the comments about the proposed rule here.
The proposed small UAS rule differs significantly from the current FAA requirements for operating UAS in the United States for commercial purposes. One of the major differences is that there will be a “UAS operator’s certificate” created so that commercial UAS pilots will no longer be required to have a FAA Pilot Certificate. Currently, the FAA requires commercial UAS pilots to have at least an FAA Sport Pilot certificate, which requires a substantial investment in money and time to achieve.
To summarize, the general proposed small UAS rules are:
UAS pilot
Must be at least 17 years old.
Must pass an aeronautical test at FAA-approved testing center, and renewed every 24 months.
Must be vetted by the Transportation Security Administration (TSA).
Must obtain an unmanned aircraft operator certificate with a small UAS rating
UAS operation
UASmust weigh less than 55 pounds.
Pilot in Command or Visual Observer must maintain visual line of sight (VLOS).
Can’t operate over people who are not part of the UAS operation.
Daylight operations only.
Yield to manned aircraft.
May use Visual Observer (VO), but not required.
First-person view camera cannot satisfy “see-and-avoid” requirement but can be used as long as requirement is satisfied in other ways.
Maximum airspeed of 100 mph.
Maximum altitude of 500 feet AGL (above ground level).
Minimum weather visibility of 3 miles from control station.
Can’t operate more than one UAS at a time.
No careless or reckless operations.
Operations in Class B, C, D and E airspace are allowed with the required ATC permission.
Operations in Class G airspace are allowed without ATC permission.
With these rules, neither a 333 Exemption nor a CoA is required, which would significantly ease the requirements for a surveying or geospatial company to begin offering UAS services.
The DJI Phantom 4 UAV.
In addition, the small UAS rule includes a framework to adapt future rules such as Micro UAS (0.55 pounds and under) rules that are being actively discussed within the FAA as well as a discussion about commercial operation of UAS over people.
In the meantime, consumer UAS are becoming more powerful with each new product introduction. DJI, the world’s largest UAS manufacturer (by far) introduced the Phantom 4. It’s a huge step forward due to one new feature: automatic collision avoidance. This feature will help operators avoid trees, buildings and potentially other UAS. I’m pretty sure this feature will eventually be included in all commercial UAS.
Intel CEO Brian Krzanich gives his keynote presentation at the 2016 Consumer Electronics Show Jan. 5, in Las Vegas, where he also announced the acquisition of Ascending Technologies for drone collision avoidance. (Photo: Intel)
Automatic collision avoidance is such a hot subject that in January, Intel acquired Ascending Technologies, a UAS manufacturer that has incorporated automatic sense and avoid technology in their UAS. According to the announcement, Intel sees “incredible opportunity for innovation across a multitude of industries. As a result, Intel is positioning itself at the forefront of this opportunity to increasingly integrate the computing, communications, sensor and cloud technology required to make drones smarter and more connected.”
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.
The U.S. Federal Aviation Administration (FAA) has released its annual Aerospace Forecast Report Fiscal Years 2016 to 2036, which finds a sustained increase in the use of unmanned aircraft systems (UAS) as well as overall air travel.
A key portion of the forecast focuses on projections for the growth in the use of unmanned aircraft, also known as drones. The FAA estimates small, hobbyist UAS purchases may grow from 1.9 million in 2016 to as many as 4.3 million by 2020.
Sales of UAS for commercial purposes are expected to grow from 600,000 in 2016 to 2.7 million by 2020. Combined total hobbyist and commercial UAS sales are expected to rise from 2.5 million in 2016 to 7 million in 2020.
Predictions for small UAS used in the commercial fleet are more difficult to develop given the dynamic, quickly evolving nature of the market. Both sales and fleet-size estimates share certain broad assumptions about operating limitations for small UAS during the next five years: daytime operations, within visual line of sight, and a single pilot operating only one small UAS at a time. The main difference in the high and low end of the forecasts is differing views on how those limitations will influence the widespread use of UAS for commercial purposes.
Looking at commercial air travel, Revenue Passenger Miles (RPMs) are considered the benchmark for measuring aviation growth. An RPM is one revenue passenger traveling one mile. The FAA forecast calls for system RPMs by mainline and regional air carriers to grow at an average rate of 2.6 percent a year between 2016 and 2036, with international RPMs projected to increase 3.5 percent a year, doubling over the forecast period. Domestic RPMs are forecast to increase by more than 50 percent over the same time. In 2015, system RPMs by U.S. carriers grew from 857 billion to 889 billion, a 3.8 percent increase.
The FAA’s NextGen program is helping to meet this consistent aviation growth. NextGen focuses on implementing technologies and procedures that utilize satellite-based aircraft navigation and phase out efficiency limitations of the current ground-based radar navigation system. For example, the environmental and economic gains of reduced fuel usage associated with NextGen advancements are projected to achieve a savings of billions of dollars in airline operational costs and achieve sustainable aviation growth.
Proven economic data that utilize sources such as generally accepted projections for the nation’s GDP are used in the FAA annual forecast, which has consistently made it the industry-wide standard of U.S. aviation-related activities. The report looks at all facets of air travel including commercial airlines, air cargo, private general aviation, and fleet sizes.
Echodyne
