Aerospace and Electronic Systems Magazine September 2016 - 4

From the Editors
Similarly, the geometry of the encounter and the algorithms necessary to detect a conflict and enact an avoidance maneuver can vary
widely in timing and detail of information required. All of these
dimensions make SAA a rich research area.
In this issue, we continue to explore these several dimensions
of SAA with five articles submitted by SAA researchers from
around the world. These articles evaluate both cooperative and
non-cooperative technology, as well as several collision avoidance
methods. These experiments include some in-the-field tests of the
SAA systems, with several notable achievements.
The first article (Wilson-"Flight Test and Evaluation of a Prototype Sense and Avoid System Onboard a ScanEagle Unmanned
Aircraft") outlines a vision-based single camera system on-board a
ScanEagle UAS. The experiments use two different lenses for the
camera to tests trade-offs in field of view and other parameters.
Algorithms for finding and tracking airborne targets were developed and were flown as a prototype system in field tests against
a Cessna aircraft. The tests evaluate whether the system provides
similar results to manned "see and avoid" capabilities.
The second article (Zsedrovits-"Onboard Visual Sense and
Avoid for Small Aircraft") also evaluates a visual system, but uses a
five camera array to provide wider field of view. These experiments
examine methods to combine the images, evaluate the environment,
and determine the potential targets. Avoidance strategies are also
explored, and some preliminary flight tests were performed, providing promising results that will be evaluated in further tests.
The third article (Liu-"An Autonomous Quadrotor Avoiding a
Helicopter in Low-Altitude Flights") explores collision avoidance

techniques for quadcopters based on cooperative ADS-B-based information. The proposed strategies are evaluated in a simulation
environment considering a low altitude scenario inspired by recent
UAS Traffic Management concepts.
The next article (Zhang-"An Advanced Sense and Collision
Avoidance Strategy for Unmanned Aerial Vehicles in Landing
Phase") specifically focuses on collision avoidance during landing
phases, adopting a biogeography-based optimization approach and
using numerical simulation studies to illustrate the effectiveness of
the developed scheme.
The final article (Kotegawa-"Proof-of-Concept Airborne
Sense and Avoid System with ACAS-XU Flight Test") provides insight into the most recent advances in the field of sense and avoid
for large UAS, describing a system developed for Predator UAS
that is based on cooperative and non-cooperative (radar) information sources, and includes ACAS-XU collision avoidance algorithms in the architecture. The article describes the systems that
were flown, how they performed, and lessons learned for future
development.
These articles delve into several of the aspects of SAA that
are of greatest interest to the research community. Various sensors,
both non-cooperative (visual-based cameras and radar) and cooperative (small transponders) are analyzed and tested for their value
in contributing to the SAA solution. Through laboratory analysis
and field test, algorithms for detecting and avoiding targets in the
environment are evaluated against the manned pilot's "see and
avoid" standard. All are contributing to the advancement of the
state of the art in UAS SAA.

IEEE A&E SYSTEMS MAGAZINE

SEPTEMBER 2016

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine September 2016