Aerial images from a drone are being evaluated as a method to survey seagrasses scarred by boat propellers.
The Texas Parks & Wildlife Department (TPWD) has partnered with Texas A&M University-Corpus Christi to determine if using unmanned aircraft systems is as effective as using planes.
Seagrasses serve as a refuge and nursery ground for fish, shrimp and crabs. They provide oxygen to the water column and serve as an area for growth of drift algae, a food source for shrimp, fish and crabs.
A law prohibiting the uprooting of seagrasses coast-wide was passed by the Texas Legislature during the 83rd legislative session and has been in effect since September 2013. Motorboats cause propeller scarring when they drift into shallow waters and tear a trough in the bay bottom.
Michael Starek, assistant professor of engineering, has been analyzing the images and data collected from flights in December of a small UAS about 450 feet above Redfish Bay’s seagrasses.
TPWD has conducted aerial surveys since 2007 using piloted aircraft flying at an altitude of about 2,000 feet, said Faye Grubbs, Upper Laguna Madre ecosystem leader with TPWD.
The project will compare the output from each method, and analyze costs of processing and ease of mobilization.
TPWD continues to collect aerial imagery in four areas along the Texas coast to evaluate the effects of the regulation. Based on the outcome of this project, Grubbs said the department may use drones for not only monitoring changes in propeller scarring, but possibly for mapping other habitats as well.
“We are comparing the accuracies of the different imagery sets — manned versus unmanned — and how well we are able to map scar features observed in the imagery,” she said.
UAS-collected imagery has the potential to change environmental monitoring at many scales, not just in coastal regions, Starek said.
The first step is to show drone-captured data is comparable to that collected from manned planes. University researchers have been doing just that for several years, flying along the coast and comparing drone-captured images to data from on-the-ground and in-the-water surveying by traditional means.
One of the biggest challenges with aerial imaging of the sea floor is weather and water clarity, Starek said.
“This experiment showed that with proper flight planning for weather conditions, mapping of prop scars with a small UAS can be a viable alternative to more costly, piloted airborne surveys,” Starek said. “Results from the flights show impressive spatial fidelity in the UAS-collected imagery. Pixel resolutions down to one inch will allow mapping of seagrass impacted by prop scaring at very fine spatial detail previously unattainable.”
Although the results show the capabilities, Starek said UAS technology still has to evolve both in platform endurance and in regulations to allow these systems to fly autonomously over much larger areas, such as an entire bay system. This latter component will evolve as the technology and confidence in its use matures.
“In the not too distant future, I can foresee the day when a fleet of small UAS equipped with cameras can routinely map an entire bay system at a fraction of the cost for traditional piloted airborne surveys,” he said. “The potential for UAS technology is immense.”
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.”
The AirMap Digital Notice and Awareness Dashboard(TM) airport manager view.
AirMap and the American Association of Airport Executives (AAAE) have released the Digital Notice and Awareness System (D-NAS), created to allow UAS operators to provide airports with real-time digital information about the location of their flights.
AirMap is a provider of airspace information and services for unmanned aircraft. On April 7, the company announced that it raised a $15 million Series A financing led by General Catalyst Partners to accelerate its global development airspace management tools for drones.
D-NAS works by allowing a UAS operator to send an encrypted digital flight notice to a secure dashboard at an airport’s operations center. Flight information can be submitted through various UAS interfaces, including the flying apps provided by drone manufacturers DJI, Yuneec and 3DRobotics.
These connections facilitate the transmission of important safety-critical information to airports, including the GPS location of the UAS flight. Participating airports will access this information through the AirMap D-NAS dashboard, which provides a map view of flights in proximity to the airport and the option to contact the UAS operator directly.
“Safety has always been a priority for DJI,” said Brendan Schulman, DJI’s vice president of Policy and Legal Affairs. “Providing our customers the capability to easily notify nearby airports of their flights is a huge step forward in convenience and functionality. A high-tech notification system complements the safety features DJI builds into every drone, as well as DJI’s close work with policymakers on practical approaches for drone technology.”
More than 50 airports across America have already joined the D-NAS pilot program, including Houston’s George Bush Intercontinental and William P. Hobby airports, Denver International Airport, Columbus Air Force Base in Mississippi, Charlotte-Douglas Airport in North Carolina, Reno-Tahoe Airport in Nevada, New Castle Airport in Delaware, Cape May Airport in New Jersey, Fairbanks International Airport in Alaska, and the Oxnard and Camarillo Airports in Ventura County, California.
The AirMap Digital Notice and Awareness Dashboard map view.
“Participation in the D-NAS pilot was a no-brainer for us. In the face of growing concerns over UAS operations near airports, AirMap has developed an effective and unique solution,” said Steve Runge, Division Manager for the Houston Airport System. “D-NAS is a game changer for how we will manage low altitude air safety.”
D-NAS not only provides heightened awareness to airports; it also makes it easier for UAS operators to comply with Section 336 of the FAA Modernization and Reform Act of 2012, which requires notice to be given to airports within five miles of a drone’s flight location.
Ben Marcus, CEO of AirMap and an airline transport rated pilot and flight instructor, said, “Everyone involved in aviation sees the promise and potential of unmanned aircraft. However, we can’t reach the potential of this amazing technology unless we ensure that safety critical information keeps pace with innovation. We are focused on building the tools for unmanned aircraft to safely integrate into the national airspace system.”
“We are excited to work with AirMap to improve the safety features of our products,” said Yuneec CEO Tian Yu. “As an airplane and helicopter pilot myself, I know first-hand how important it is to keep the national airspace system safe.” Yuneec is the manufacturer of the Typhoon and Tornado series of multirotor drones and recently announced a $60M investment from Intel.
The Federal Aviation Administration (FAA) is now reviewing the report and recommendations of the Micro Unmanned Aircraft Systems (UAS) Aviation Rulemaking Committee. The agency had tasked the group to develop recommendations for performance-based regulations that would let certain unmanned aircraft operate over people not directly involved in the flight of the aircraft.
The rulemaking committee, which began meeting March 8, worked under a tight deadline of April 1 to deliver its report. The task force included a diverse set of aviation stakeholders, including UAS manufacturers, UAS operators, consensus standards organizations, researchers and academics.
“We commend the committee members for their sincere dedication and for producing a comprehensive report in such a short time,” said FAA Administrator Michael Huerta. “This type of collaborative government and industry partnership is exactly what is needed to keep pace with this rapidly changing industry and will serve as a model for future rulemaking advisory tasks.”
The ARC’s consensus report recommends establishing four small UAS categories, defined primarily by risk of injury to people below the flight path. For each category, the group recommends assigning a potential risk linked to either weight or impact energy. The report also addresses operational restrictions and standards to minimize the risks associated with each category.
The FAA will use the information in the report to develop a flexible, performance-based proposed rule. The public will have an opportunity to comment on the proposal based on the ARC’s recommendations.
A UAV flight demonstration by Paul Beckwith of DataCapable, a former Navy Civil Engineer, uses the drone cage at the Esri Federal GIS Conference, held Feb. 24-25 in Washington, D.C.
Illustration of the contemplative real-time (CRT) window measurement timeline. The window contains a prior for the initial state, K GPS measurements, and many IMU measurements between each pair of GPS measurements. IMU measurement times are indicated as dots on the timeline. All of these items yield constraints on the estimated trajectory ˆX during the CRT window.
Integer ambiguity resolution in multi-epoch GPS/INS
A novel integer ambiguity resolution approach over a time window of GPS/IMU data enhances the reliability of obtaining high-accuracy position estimation, using carrier phase measurements, even in challenging environments. The method focuses on reducing computational cost. The achievable savings should be on the order of 104, while 600 has been demonstrated. The theoretical approach shows that the cost function can be decomposed into one part that determines the shape and vicinity of the trajectory, but is insensitive to the carrier phase integers and a position shift vector, and a second part that is sensitive to the carrier phase integer and can be solved to determine the required position shift so that the location of the trajectory is accurately known.
By Yiming Chen, Sheng Zhao, and Jay A. Farrell, University of California, Riverside.
Indoor Flight Demonstration Results of an Autonomous Multi-copter Using Multiple Laser Inertial Navigation, by Adam Schultz, Russell Gilabert, and Maarten Uijt de Haag, Ohio University.
This paper discusses aspects of autonomy on a small-size multi-copter UAS for challenging environments, addresses in detail the modified proposed navigation algorithm, its integration with the flight controller for autonomous flight and the actual implementation on the multi-copter platform. The paper includes flight test results of a multi-copter UAS operating in an outdoor/indoor environment and shows some navigation and mapping performance results.
Q: What is the “killer app” for professional use of drones? What UAV market sector will most powerfully drive adoption and influence new regulations?
Jan Leyssens Product Manager, SeptentrioA: The mapping market is opening up. On construction and mining sites, surveyors walk between dozers and dump trucks to create digital terrain models, a time-consuming and dangerous job, which drones can do more efficiently and safely. These jobs are performed in non-public areas, without significant risks or privacy concerns, facilitating public acceptance. Subsequently the potentially larger inspection market will open up. Drones provide an easy, safe way to inspect wind turbines or other installations that are difficult or dangerous to reach.
Tony Murfin Contributing Editor, Professional OEM & UAV, GPS WorldA: The agriculture industry seeks even greater Improvements in crop yields. GNSS systems in the cabs of combines/harvesters have already helped significantly, but drone use for crop-growth monitoring, data collection and pesticide-prescription application is the big breakthrough — once rules for large-scale low-level drone flight over farmland are approved. Ag will push for published rules just as hard as the movies, real-estate and all types of aerial survey for construction and utilities.
Eric Gakstatter Contributing Editor, GIS & UAV, Geospatial SolutionsA: Amateur photographers and hobbyists are where the volume is. The world’s largest UAV manufacturer now exceeds $1B annual revenue. Its growth is being driven by the hobby market. Commercial use of UAVs is a very small piece of the worldwide UAV market. The UAV market will be very similar to the GPS receiver market, just not at the same scale. The volume in the UAV consumer market will drive the technology (sensors, motors, software) that will benefit commercial UAV manufacturers.
Global drone manufacturers 3DR, DJI, GoPro and Parrot today are forming the Drone Manufacturers Alliance, a coalition intended to serve as the voice for drone manufacturers and their customers across civilian, governmental, recreational, commercial, nonprofit and public safety applications.
“We will advocate for policies that promote innovation and safety, and create a practical and responsible regulatory framework,” said Kara Calvert, director of the Drone Manufacturers Alliance. “There are significant economic and social benefits to drone operations in the U.S., and industry must work with policymakers to ensure a safe environment for flying.
“The Drone Manufacturers Alliance believes a carefully balanced regulatory framework requires input from all stakeholders and must recognize the value and necessity of continued technological innovation. By highlighting innovation and emphasizing education, we intend to work with policymakers to ensure drones continue to be safely integrated into the national airspace.”
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 VN-360 OEM GPS-Compass module provides an accurate, True North heading solution for systems integrators seeking a reliable alternative to magnetic-based sensors to improve the capabilities and performance of next-generation manned and unmanned systems. Unlike digital magnetometers that can be affected by ferrous materials, the VN-360 heading solution provides a cost-effective GPS-based alternative. With two onboard GNSS receivers, the VN-360 calculates the relative position between its two GNSS antennas to derive a heading solution an order of magnitude more accurate than a magnetic compass. It supports a variety of GNSS antennas that can be mounted on the host platform with a separation distance from a few centimeters to several meters. Applications include antenna pointing, multirotor UAVs and aerostats, automated agriculture, heavy machinery, ground robots, weapons training, warfare simulation and direct surveying.
The SDX software-defined GNSS simulator is now available in version 16.2. For real-time kinematic application, it is now possible to synchronize multiple simulators using a 10-MHz reference and pulse-per-second (PPS) signal. Users can modify pseudorange from the graphical user interface or the application program interface (API) in real time. Each satellite can be controlled individually or together. Trajectories can be imported from CSV files, and raw datalogging is improved. The navigation message can be changed in real time during the simulation. There is now an alternative to python API with the C++ open source API (other programming languages, such as C#, will be supported in the future.)
Designed for hydrographic tasks from shallow to deep water
The Apogee-M motion reference unit and the Apogee-U inertial navigation system (INS) are both made of titanium and have a depth rating of 200 meters. The Apogee Series is an accurate INS based on robust micro-electro-mechanical systems (MEMS) technology with a high degree of precision — 0.008 degrees in roll and pitch in real time — while delivering a robust and accurate heading from the continuous fusion of GNSS and IMU data. Apogee-M and Apogee-U are designed to mount close to the sonar head for hydrographic tasks in shallow or deep water. They provide a real-time heave accurate to 5 centimeters, which automatically detects the wave frequency and constantly adjusts to it. When wave frequency is erratic or in case of long-period swell, the delayed heave feature can allow survey in rough conditions with a more extensive calculation, resulting in a heave accurate to 2 cm displayed in real-time with a short delay. Apogee sensors can be paired with any survey-grade GNSS receiver or with one offered by SBG Systems.
The Piksi is a high-performance GPS receiver with real-time kinematic (RTK) functionality for centimeter-level relative positioning accuracy. Designed for integration into autonomous vehicles and portable surveying equipment, it has a fast position-solution update rate and low-power consumption in a small form factor. An open-source architecture with a high-performance digital signal processor on board and a flexible correlation accelerator make it suitable for GNSS research. Features include centimeter-accurate relative positioning (carrier-phase RTK); GPS, GLONASS, Galileo and SBAS signals; 50-Hz position/velocity/time solutions; and integrated patch antenna and external antenna input.
BYOD program offers a range of configurations for a variety of jobs
Anatum Field Solutions (AFS) has launched a nationwide Bring Your Own Device (BYOD) submeter GNSS and centimeter real-time kinematic (RTK ) GNSS receiver rental program. AFS rentals target high-accuracy users in GIS, UAV, environmental, engineering, surveying, agriculture, electric/gas/water utilities, pipeline, forestry, mining, transportation, construction, architecture and government markets. AFS offers all mobile GIS devices including Apple iOS, Android, Windows and Windows Mobile/EHH. It also stocks various GNSS receivers such as Eos Arrow (submeter and centimeter), SXBlue (submeter and centimeter), Trimble R1 (1 meter) and BadElf (1–3 meters) in a variety of configurations.
The FC-5000 field controller, with its 7-inch sunlight-readable display, is designed to provide operators a larger, more versatile and faster handheld computer for the modern construction site. The display has a capacitive touch interface — with finger, glove, small-tip stylus and water-capable options — that is optically bonded to increase visibility. With the press of a key, a user can change the orientation of the screen from portrait to landscape to increase visibility when viewing maps or drawings. The controller is compatible with all Topcon GNSS receivers and total stations, operating MAGNET Field, Site and Layout software. It has two built-in cameras: an 8-MP camera with autofocus and LED flash for field photography, and a 2-MP camera on the front for video meetings. Additional features include 64 GB of flash storage, an optional 4G LTE cellular modem, internal GPS navigation, Bluetooth and Wi-Fi, and a battery life of 10-plus hours.
Phantom 4 features obstacle avoidance, active tracking
The Phantom 4 quadcopter uses advanced computer vision and sensing technology to make professional aerial imaging easier. Its onboard intelligence makes piloting and shooting images easier through features such as its Obstacle Sensing System and ActiveTrack functionality. The Obstacle Sensing System features two forward-facing optical sensors that scan for obstacles and automatically direct the aircraft around impediments, reducing risk of collision, while ensuring flight direction remains constant. Obstacle avoidance also engages if the user triggers the drone’s “Return to Home” function to reduce the risk of collision when automatically flying back to its takeoff point. With ActiveTrack, the user can keep the camera centered on a subject. ActiveTrack allows users running the DJI Go app on iOS and Android devices to follow and keep the camera centered on the subject as it moves by tapping the subject on their smartphone or tablet.
The Pteryx UAV is a photomapping tool designed to help with photogrammetry, land property surveillance, environmental survey, search and rescue, precision agriculture, research, and in the energy sector. With a two-hour flight time, missions can be planned with the endurance reserve needed to overcome the large distances and worst-case changing weather conditions. Pteryx is designed to fly at speeds of about 50 km/h in light or medium wind speeds. The Pteryx can lift up to 1 kilogram of cargo: cameras, camcorders or other research equipment. The payload is housed in a roll-stabilized head on the front of the fuselage. The Pteryx can also accommodate a wide variety of sensors, which are installed in an easy to replace camera head. The Pteryx is delivered with a 16 MPx APS-C (crop sensor) daylight camera and wide lens, with other sensor options available.
The M2-D is a miniature stabilized gyro with electro optical (EO) and infrared imagers. The system is designed for mobile, marine and aerial unmanned applications. The M2-D is compact at 3 inches tall and 2 inches in diameter. The gimbal is fully gyro stabilized and packs sensor technologies previously only available in much larger payloads. The infrared FLIR brand pan-tilt-zoom thermal imaging camera has an optical telephoto zoom in a lightweight 160-gram payload. The high-resolution thermal imaging sensor with digital zoom integration lets users capture stable video in total darkness. For daytime operations, the gimbal has a full-color visual camera with optical 6x zoom to ~4 degrees. The optical zoom is then enhanced with digial zoom integration for stable long-range imaging.
Surveys large areas or objects to generate fast, precise data
Version 2 of the AibotX6 hexacopter features high-precision (HP) GNSS for surveyors. The system also can be installed in existing AibotX6 hexacopters. With Version 2, the precision and quality of surveying data is significantly improved with RTK technology based on the Leica Geosystems SmartNet correction data service. Post-processing is also possible. The new AibotX6 HP GNSS workflow guarantees precision of up to 2-centimeter position accuracy. Besides allowing the use of existing surveying hexacopters, continuing generation and processing of data can be done with the fully integrated software Aibotix AiProFlight. The Aibot X6 can carry a variety of sensors weighing up to 2 kilograms.
Integrates GNSS for challenging maritime positioning
The new DPS 432 combines full decimeter accuracy with high integrity and availability of GNSS data, supporting the safety and efficiency of offshore operations that rely on advanced dynamic positioning (DP) systems. It integrates signals from GPS, GLONASS, BeiDou and Galileo, and regional correction signals including SBAS and G4 services from Fugro, to ensure high flexibility for DP operations globally. Suited to complex operations, the system increases satellite availability, improves integrity monitoring and enables more precision under challenging signal tracking conditions. The DPS 432 features a sophisticated engine that runs in a safe mode protected from unintended user operations.
The aera 660 features a 5-inch capacitive touchscreen display that has been optimized for cockpits and various types of flying. It has a built-in GPS/GLONASS receiver and rich, interactive maps that can be viewed in portrait or landscape modes. Cost-effective database options along with Wi-Fi database updating capabilities allow customers to access up-to-date data, including daily U.S. fuel prices. Bluetooth supports the display of ADS-B in traffic and weather from a variety of sources, including the GDL 39/GDL 39 3D, Flight Stream and the GTX 345 ADS-B transponder. The aera 660 withstands the harshest environments, meeting stringent temperature tests and helicopter vibration standards. Depending on settings and external connections, pilots can receive up to four hours of battery life on a single charge.
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.
Starting March 31, owners of small unmanned aircraft systems (UAS) used for commercial, public and other non-model aircraft operations will be able to use the FAA’s new, streamlined, web-based registration process to register their aircraft.
The web-based process will significantly speed up registration for a variety of commercial, public use and other users. Registration for those users is $5, the same fee that model aircraft owners pay.
“Registration is an important tool to help us educate aircraft owners and safely integrate this exciting new technology into the same airspace as other aircraft operations,” said FAA Administrator Michael Huerta.
All owners of small UAS used for purposes other than as model aircraft must currently obtain a 333 exemption, a public certificate of authorization or other FAA authorization to legally operate, in addition to registering their aircraft. Before today, the FAA required all non-hobby unmanned aircraft owners to register their aircraft with the FAA’s legacy aircraft registry in Oklahoma City, Oklahoma.
Those owners who already have registered in the legacy system do not have to re-register in the new system. However, the FAA is encouraging new owners who are registering for the first time to use the new, web-based registration system.
Owners who register under the new system can easily access the records for all of the aircraft they have registered by logging into their on-line account.
Small UAS owners who have registered under the web-based system who intend to use their aircraft for purposes other than as model aircraft will also need to re-register to provide aircraft specific information.
The FAA first opened up the web-based registration for model unmanned aircraft owners on Dec. 21, 2015.
The agency is expanding that existing website to accommodate owners of aircraft used for purposes other than model aircraft. This registration process includes additional information on the manufacturer, model and serial number, in addition to the owner’s physical and email addresses. Like the model aircraft registration process, a certificate is good for three years, but each certificate covers only one aircraft.
A law prohibiting the uprooting of seagrasses coast-wide was passed by the Texas Legislature during the 83rd legislative session and has been in effect since September 2013. Motorboats cause propeller scarring when they drift into shallow waters and tear a trough in the bay bottom.
Indoor Flight Demonstration Results of an Autonomous Multi-copter Using Multiple Laser Inertial Navigation, by Adam Schultz, Russell Gilabert, and Maarten Uijt de Haag, Ohio University.
