One of the major topics in unmanned vehicles operations is the
lack of effective sense-and-avoid (SAA) capabilities of the UAS (Oliver, 2016).
A key challenge within the SAA problem is to reliably and automatically detect
potential midair aircraft collisions (Bratanov, Mejias, & Ford, 2017). The ability of the UAS to sense and avoid
surrounding traffic must be fully addressed before the UAS can be integrated
into the National Aerospace System. As we know, most manned aircraft carry
advanced traffic collision avoidance technologies (Federal Aviation
Administration [FAA], n.d.). However, UAS are not currently required to
incorporate any sense-and-avoid equipment. Piloted aircraft are required to
maintain a visual scan for traffic at all times. However, UAS may be difficult
to detect due to their small size. Detection is even more difficult in poor
meteorological conditions. Since there is no pilot directly at the controls of
the UAS, visual traffic detection may be inadequate due to the limited field of
view, control signal latency, and other technological constraints. During
manned aircraft operations, the pilots are directly responsible to visually
detect, avoid, and maintain a safe distance from the surrounding traffic
(Consiglio, Chamberlain, Munos, & Hoffler, 2012). Manned aircraft employ a
variety of methods for traffic separation. Pilots rely on visual cues, air
traffic control (ATC) advisories, and other sensory information available in
the cockpit.
The FAA has restricted UAS
operations below 400 feet above ground level (AGL), and within the line of
sight of the pilot, and in fair meteorological conditions (FAA, 2016). These
limitations may help in separating UAS from piloted aircraft to some degree.
Nevertheless, these restrictions do not provide the acceptable level of safety.
Eventually, UAS missions will have to be extended to altitudes beyond 400 feet
AGL and UAS will be operating alongside piloted aircraft in all airspace
segments.
The lack of UAS SAA capability and its adverse effects on aviation
safety has been a subject of research. The first SAA alternative offered by
scholars is a ground-based SAA (GBSAA). This method employs the UAS pilot
housed in the ground control station (GCS) as the primary authority for
detection, evaluation, and execution of the traffic avoidance maneuvers. The
traffic information would be displayed on the screen in the GCS. The UAS pilot
will also rely on ATC traffic advisories and alerts and, if necessary, follow
the ATC recommendations to avoid the surrounding traffic.
The second alternative action for SAA problem mitigation is to
incorporate traffic detecting and avoidance technology directly onboard of the
UAS. There are a variety of SAA sensor options available. SAA sensors can be
grouped into two categories: cooperative and non-cooperative technologies
(Albaker & Rahim, 2011). Cooperative sensors require the installation of
transponder equipment on board the aircraft to broadcast its position
information and interrogate surrounding traffic (Asmat et al., n.d.).
Cooperative sensors will only function if all participating aircraft are
equipped with transponders (Fasano, Accardo, Tirri, Moccia, & DeLellis,
2015). On the other hand, non-cooperative sensors are capable to detect
airborne targets autonomously, regardless of whether the intruder aircraft carry
any SAA equipment or regardless of transponder installation (Asmat et al.,
n.d.).
A couple examples of cooperative technologies are the Automatic
Dependent Surveillance-Broadcast (ADS-B) and the Traffic Alert and Collision
Avoidance System (TCAS). ADS-B and is a part of the NexGen ATC system
(Zimmerman, 2013).
Figure 1. ADS-B diagram. ADS-B includes ground stations, GPS, and
aircraft avionics. Adapted from “ADS-B 101: What is it and why you should
care,” by J. Zimmerman, 2013. Copyright 2013 by J. Zimmerman.
Figure 2. TCAS Version 7.1 with smart reversion logic allows pilot
to properly select the corrective maneuver, avoid overcorrection, and reverses
resolution advisories in accordance with intruder aircraft maneuvering. Adapted
from “TCAS II Version 7.1,.” by Eurocontrol, 2014. Copyright 2014 by
Eurocontrol.
Another option is to use
non-cooperative sensors for UAS SAA. Many researchers have focused more on
non-cooperative sensors of the active and passive type as they can provide
better detection of the non-cooperative traffic (McClellan, Kang, &
Woosely, 2017). There are a variety of technologies currently available, each
with its specific advantages and drawbacks (Yu & Zhang, 2015). The main
advantage of non-cooperative sensors is their ability to detect the intruder
regardless of what equipment is installed on the other aircraft. Therefore,
non-cooperative technology is useful if other traffic does not have a
transponder or the ADS-B equipment. This sensor category includes the
following: thermal, electro-optic/infrared (EO/IR), acoustic, laser obstacle
avoidance system (LOAM), millimeter wave radar (MMW), and synthetic aperture
radar (SAR).
Sensor fusion is another
approach, which combines cooperative and non-cooperative technologies to
compensate for limitations of the sensing systems. The research and development
in sensors fusion are however still in its initial stages. Using both
cooperative and non-cooperative airborne sensor in combination with
ground-based traffic surveillance will increase the UAS SAA capability and,
therefore, raise the levels of operational safety. Researchers have need
testing and suggesting various sensor combinations in different SAA scenarios
and evaluating the capabilities of various technology.
Another technological challenge
in SAA is to meet the size, weight, and power (SWaP) limitations of UAS and
especially small-UAS while still maintaining the needed sensing capability.
Some researchers propose the use of miniaturized airborne radar for automated
traffic detection and avoidance (Roberts, 2017).
SAA capability should become
a major prerequisite for UAS operations in the NAS. SAA capability should be
considered a minimum performance requirement for unmanned aircraft. It is
important to test the SAA algorithms for different flight scenarios. For
example, different aircraft convergence situation should be tested, such as
head-on approach, climbing from below, or descending from above. It would be
advantageous to perform SAA testing in the various weather conditions. For
instance, daylight visual flight rules (VFR) and night VFR. Simulation and
actual flight testing should be conducted with different UAS groups to
determine that the SAA system meets the required levels of safety (Kuchar,
n.d.).
UAS SAA is an overwhelming
problem being discussed among aviation regulatory and safety agencies. UAS
proliferation is rapidly increasing in the civilian sector, and it is
imperative to address a means to incorporate SSA for safe UAS operation.
Standardized equipment mandates, UAS certification, and pilot training for SAA
scenarios should be established and enforced. Proper standards should be set to
assure that UAS collision avoidance performance equals to that of the manned
aircraft collision avoidance capabilities. The FAA should revise some of the
regulatory documentation to include proper amendments for UAS operations.
UAS integration into the NAS should not compromise safety or efficiency of the airspace operations. UAS will have to adapt to the standards and procedure currently employed in the NAS. However, it is probable that the current rules and regulations for manned aircraft will have to be adjusted to include the new UAS members. Only then we will be able to take a full advantage of the benefits UAS offer.
UAS integration into the NAS should not compromise safety or efficiency of the airspace operations. UAS will have to adapt to the standards and procedure currently employed in the NAS. However, it is probable that the current rules and regulations for manned aircraft will have to be adjusted to include the new UAS members. Only then we will be able to take a full advantage of the benefits UAS offer.
References
Albaker, B. M., & Rahim, N. A. (2011). A conceptual framework and a review of conflict sensing, detection, awareness and escape maneuvering methods for UAVs. Retrieved from UMPEDAC Research Centre, Faculty of Engineering, University of Malaya: http://www.intechopen.com/books/aeronautics-and-astronautics/a-conceptual-framework-and-a-review-of-conflict-sensing-detection-awareness-and-escape-maneuvering-m
Asmat, J., Rhodes, B., Umansky, J., Villlavicencio, C., Yunas, A., Donohue, G., & Lacher, A. (n.d.). UAS safety: unmanned aerial collision avoidance system. Retrieved from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4055110
Bratanov, D., Mejias, L., & Ford, J. (2017). A vision-based sense-and-avoid system tested on a ScanEagle UAV. International Conference on UAS. https://doi.org/10.1109/ICUAS.2017.7991302
Eurocontrol. (2014). TCAS II Version 7.1. Retrieved from http://www.eurocontrol.int/articles/tcas-ii-version-71
Federal Aviation Administration. (2016). Summary of small unmanned aircraft rule (part 107). Retrieved from http://www.faa.gov/uas/media/Part_107_Summary.pdf
Federal Aviation Administration. (n.d.). 14 CFR 91.227 - Automatic Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements. Retrieved from https://www.law.cornell.edu/cfr/text/14/91.227
Kuchar, J. K. (n.d.). Safety analysis methodology for unmanned aerial vehicle (UAVs) collision avoidance systems. Retrieved from Massachusetts Institute of Technology: http://www.ll.mit.edu/mission/aviation/publications/publication-files/ms-papers/Kuchar_2005_ATM_MS-19102_WW-18698.pdf
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