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The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser radar (LADAR or LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LADAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in military low-level flying platforms such as helicopters and small-size Unmanned Aircraft (UA). In this paper we describe the design and test activities performed to develop the Laser Obstacle Avoidance System “Marconi” (LOAM). After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and data processing algorithms developed for obstacle detection, classification and calculation of alternative flight paths, as well as to the ground and flight test activities performed on various platforms.
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Abstract
The availability of powerful eye-safe laser sources and
the recent advancements in electro-optical and mechanical beam-
steering components have allowed laser-based Light Detection and
Ranging (LIDAR) to become a promising technology for obstacle
warning and avoidance in a variety of manned and unmanned aircraft
applications. LIDAR outstanding angular resolution and accuracy
characteristics are coupled to its good detection performance in a
wide range of incidence angles and weather conditions, providing an
ideal obstacle avoidance solution, which is especially attractive in
low-level flying platforms such as helicopters and small-to-medium
size Unmanned Aircraft (UA). The Laser Obstacle Avoidance
Marconi (LOAM) system is one of such systems, which was jointly
developed and tested by SELEX-ES and the Italian Air Force
Research and Flight Test Centre. The system was originally
conceived for military rotorcraft platforms and, in this paper, we
briefly review the previous work and discuss in more details some of
the key development activities required for integration of LOAM on
UA platforms. The main hardware and software design features of
this LOAM variant are presented, including a brief description of the
system interfaces and sensor characteristics, together with the system
performance models and data processing algorithms for obstacle
detection, classification and avoidance. In particular, the paper
focuses on the algorithm proposed for optimal avoidance trajectory
generation in UA applications.
Keywords
LIDAR, Low-Level Flight, Nap-of-the-Earth Flight,
Near Infra-Red, Obstacle Avoidance, Obstacle Detection, Obstacle
Warning System, Sense and Avoid, Trajectory Optimisation,
Unmanned Aircraft.
I. I
NTRODUCTION
OW level navigation and terrain-following operations
with UA are challenged by a variety of natural and man-
made obstacles, as well as by adverse weather conditions that
can significantly reduce the obstacles visibility. Reduced
visibility is the main factor preventing the UA pilot from
safely controlling the aircraft and from identifying possible
obstacle collision hazards. In these scenarios, radar has been
traditionally employed to automatically maintain a safe
separation from the ground by flying the aircraft at a certain
altitude above the terrain. However, state-of-the-art avionics
R. Sabatini is Associate Professor in the School of Aerospace, Mechanical
and Manufacturing Engineering, RMIT University, Melbourne, VIC 3000,
Australia. Formerly, he was a Senior Officer and Experimental Flight Test
Engineer in the Italian Air Force, Pratica di Mare AFB, 00040 Pomezia (RM),
Italy (phone: +61 3 9925 8015; e-mail: roberto.sabatini@rmit.edu.au).
A. Gardi is a doctoral research student in Aerospace Engineering in the
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT
University, Melbourne, VIC 3000, Australia.
M. A. Richardson is Full Professor and Head of the Sensors and Systems
Engineering Department at Cranfield University, Defence Academy of the
United Kingdom, Shrivenham, Swindon, UK.
radars lack sufficient angular resolution to be able to detect
small natural and man-made obstacles such as trees, power
line cables and poles. The outstanding angular resolution and
accuracy characteristics of Light Detection and Ranging
(LIDAR), coupled to its good detection performance in a wide
range of incidence angles and weather conditions, provide an
ideal solution for obstacle avoidance, which is very attractive
in low-level flying Unmanned Aircraft (UA).The first laser
experiment directed towards a laser obstacle detection and
avoidance system started in 1965 with a Nd:YAG laser [1].
This system demonstrated the feasibility of using lasers to
detect obstacles such as wires. Semiconductor lasers, such as
GaAs and GaAlAs have been experimented with since 1966
[2]. These lasers radiate in the wavelength region of 0.84 to
0.9µm. The experience gained with these experimental
systems pointed out many features that are now being
incorporated into present day research. Due to eye-safety and
adverse weather (fog) propagation concerns, further
development with Nd:YAG and the various semiconductor
lasers has been substantially reduced, in favor of CO
2
lasers.
One of the first heterodyne detection CO
2
system was the
LOWTAS, developed by UTRC. More recent developments
include CLARA, the Anglo-French compact laser radar
demonstrator program [3]; HIWA, a German system built and
tested by Eltro and Dornier [4], and OASYS, developed in the
U.S. by Northrop [5]. Current research efforts are
concentrating on 1.54µm (frequency-shifted Nd:YAG and
Er:glass) solid state lasers. One 1.54µm system has been
developed for the Italian Military Forces by Marconi S.p.A., in
cooperation with the Air Force Flight Test Centre. The
equipment, here named LOAM (Laser Obstacle Avoidance
Marconi), is a low-weight/volume navigation aid system for
rotary-wing/UA platform specifically designed to detect
potentially dangerous obstacle placed in or nearby the flight
trajectory and to warn the crew in suitable time to implement
effective avoiding maneuvers. The first airborne prototype of
LOAM was assembled in 2005 and extensive laboratory and
field tests were performed on the various sub-units, in order to
refine the system design (both hardware and software
components). Furthermore, the overall system was tested in
flight on helicopter test-bed platforms [6], [8]. Current
research is focusing on the development of a scaled LOAM
variant for small to medium size UA applications.
II. O
PERATIONAL
R
EQUIREMENTS
As discussed in [6], the first and most important
requirement for an airborne Obstacle Warning System (OWS)
to be effective is reliable detection of all relevant obstacles in
Roberto Sabatini, Alessandro Gardi, Mark A. Richardson
LIDAR Obstacle Warning and Avoidance System
for Unmanned Aircraft
L
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a wide envelope of conditions defined primarily by the
available laser range performance at various incidence angles
and by the system Field-of-View (FOV). These parameters
must be consistent with the aircraft dynamics envelope and
produce a very high probability of detection and a very low
false alarm rate. By all relevant obstacles, it is meant terrain
masses, buildings, poles, towers, power cables and indeed any
structure which may pose a hazard to low-level flying aircraft.
The need for a high probability of detection is obvious since
no real obstacle threat must go undetected. A low false alarm
rate is required to prevent spurious warnings that would
increase the pilot's workload unnecessarily and possibly cause
the pilot to raise his altitude without real need, thus making
him a better target in military operations requiring
electromagnetic covertness. Another operational requirement
is the minimum detection range. This will depend upon the
aircraft speed, climb angle capability (different for helicopter,
UA and airplane platforms), and pilot reaction time. As an
example, for an airplane flying straight and level at 300m/sec
and allowing a reasonable pilot reaction time and aircraft
response time of between five to ten seconds, detection ranges
of about two to three kilometers are adequate. For helicopter
and UA applications, this range is generally reduced by an
order of magnitude or more. The system should, ideally,
perform all of its required functions in all weather, day and
night. In practice however, laser radiation is not capable of all-
weather operation and the best trade-off of system
characteristics must be looked at.
III. LOAM
S
YSTEM
D
ESCRIPTION
LOAM system was originally developed for military
rotorcraft platforms and, in the current study, we discuss some
of the key aspects related to its potential design evolutions for
integration in civil and military UA platforms. The general
architecture of LOAM is depicted in Fig. 1.
Fig. 1 General architecture of LOAM, adapted from [6]
LOAM is capable of detecting obstacles placed in or nearby
the aircraft trajectory, classifying/prioritizing the detected
obstacles, and providing obstacle warnings and information to
the crew (both aural and visual). As depicted in Fig. 2, the
laser beam scans periodically the area around the flight
trajectory inside a Field of View (FOV) of 40° in azimuth and
30° in elevation with field of regard capability of ±20° both on
azimuth and elevation, centered on the optical axis of the
system).
Fig. 2 LOAM Horizontal and Vertical FOV
LOAM also allows the operator to select the azimuth
orientation of the FOV among three different directions, and
in particular oriented either in the same direction of the
platform heading (normal flight envelope), or 20° left/right
with respect to the platform heading (to optimize coverage
during turning maneuvers at high angular speed). This is
represented in Fig. 3.
Fig. 3 LOAM FOV Orientation
During every scan (4 Hz repetition frequency), the laser
beam changes its orientation producing an elliptical pattern
across the FOV with the characteristics shown in Fig. 4.This
scanning pattern is well suited to detect the most dangerous
obstacles, like wires, due to the several and regularly spaced
vertical lines that it produces. Additionally, it can be obtained
by using very reliable scan mechanical devices with reduced
weight.
Fig. 4 LOAM Scan Pattern
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Using dedicated signal processing algorithms optimized for
low-level obstacle detection, the system holds an inherently
high capacity to detected various types of obstacle
independently from the platform motion during the frame
acquisition period, providing the possibility of reconstructing
the obstacle shape without using navigation data (stand-alone
integration) in slow-moving platforms with a benign attitude
envelope. Additionally, LOAM can be integrated with the
aircraft navigation sensors if required, especially in platforms
with high dynamics envelopes [8].LOAM performs echo
detection through an analogue process comprising an optical-
electrical conversion, a signal pre-amplification and a
threshold comparison. The signal pre-amplification is
achieved by an automatic controlled gain amplifier to increase
the system sensitivity as the elapsed time from the laser
emission increases in order to adjust the sensitivity on the
basis of the expected return signal power in connection with
the obstacle range. Furthermore, an adjustable threshold level
is provided to take into account the background conditions.
These features reduce the probability of false echo detection
due to the atmospheric back-scatter near the laser beam output
and optimize the system sensitivity in various operational
weather conditions. LOAM performs echo analysis in order to
determine the presence of possible obstacles and to determine
their geometrical characteristics and position. For this purpose,
LOAM operates through two sequential analysis processes:
Local Analysis (LAN) and Global Analysis (GAN).The LAN
process is performed on the single echoes in order to
determine range, angular coordinates and characteristics of the
obstacle portion generating them. The GAN process manages
groups of echoes, detected during a scan period, with the
related information provided by the LAN process, in order to
perform the obstacle detection as a whole and determine the
related shape and type. LOAM is capable to automatically
classify obstacles according to the following classes [8]:
1) Wire. This class groups all thin obstacles like wires and
cables (e.g., telephone/electrical cables and cableway);
2) Tree. This class groups vertical obstacles of reduced
dimensions (e.g., trees, poles and pylons);
3) Structure. This class groups extended obstacles (e.g.,
bridges, buildings and hills).
Fig. 5 represents LOAM detection algorithm structure.
Furthermore, LOAM performs automatic prioritization of the
detected obstacles in function of the risk represented
according to the relevant range, and provides the crew with
timely warnings and information of the detected obstacles in
order to allow the implementation of effective avoidance
maneuvers. For this purpose, LOAM system is able to deliver
both visual and audio warnings. LOAM information relative to
the detected obstacles can be provided on a dedicated display
(NVG compatible) whose screen represents the FOV of the
system.
Fig. 5 Structure of LOAM detection algorithm
Both 3-D and 2-D are possible, together with an altimetric
profile format. An example of a 3-D LOAM display format
implemented for the already developed helicopter applications
is shown in Fig. 6 [6]. The corresponding 2-D and altimetric
display formats are shown in Figs. 7 and 8 respectively. The
general architecture of LOAM system was described in [6].
LOAM main components are the Sensor Head Unit (SHU),
the Control Panel (CP) and the Display Unit (DU). In the
following paragraph a brief description of LOAM SHU is
given, together with an outline of the main EPU functions.
Fig. 6 LOAM 3-D Display Format
Fig. 7 LOAM 2-D Display Format
FOV centre
Platform axes
Direction of flight
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Fig. 8 LOAM Altimetric Display Format
The SHU performs the following main functions [8]:
1) generates a laser beam and scan the area around the flight
trajectory;
2) detects return echoes;
3) analyzes detected echoes in order to compute range,
coordinates and local geometrical characteristics
(attributes) of the obstacles they come from;
4) communicates echo range, coordinates and attributes to
LOAM Processing Unit, or to other on board systems, via
a RS-422 high speed serial data link.
The SHU scans a laser beam in the area around the flight
trajectory, performs echo detection through an analogue
process comprising an optical-electrical conversion (by means
of an avalanche photodiode - APD), a signal pre-amplification
and a threshold comparison (adjustable threshold).The SHU
performs echo analysis in order to compute range, coordinates
(azimuth, elevation with respect to LOAM reference frame)
and local geometrical characteristics (attributes) of the
obstacles they come from. A detailed description of
LOAMSHU architecture is presented in [6]. Some key electro-
optical parameters relative to the laser sub-unit are listed in
Table I.
TABLE
I
L
ASER
P
ARAMETERS
Parameter Description Value
Wavelength Laser emission wavelength 1.55
µ
m
Peak Power Laser pulse power at the laser assembly output
10 kW
Pulse Duration
Laser pulse duration 3 to 5 ns
Frequency Laser pulse repetition frequency 60 kHz
IV. C
ALCULATION OF
A
VOIDANCE
T
RAJECTORIES
Once the obstacle has been detected and processed as
described, LOAM system triggers the generation of an
avoidance trajectory, based on the own vehicle dynamics and
the obstacle relative equation of motion. Due to the restricted
system FOV, some information, acquired in the previous
frame, may be lost successively. To keep obstacles
information when they are outside the present frame, it is
necessary to store the position of every object detected and
then update the coordinates with respect to the platform body-
fixed reference system. LOAM history function stores the data
of the obstacles for a defined time interval and deletes them
when they are outside the platform possible trajectories
(outside its flight envelope).
Since the motion data supplied from navigation system are,
like every measure, affected by an error, we evaluate how
these errors affect the positions calculated for every obstacle.
To do so, a Gaussian error is added to every data and a
statistic of the position error is calculated for obstacles near
and far from the aircraft. When the impact warning processing
establishes that the trajectory currently flown by the aircraft
has a collision risk, the algorithm searches the corrections
necessary to avoid the obstacles, and provides the pilot an
indication about the alternative (optimal) direction to fly. The
optimal trajectory is the one having the smaller possible
correction (necessary to avoid the obstacles) and which is
compatible with a safe flight path.
The original avoidance trajectories generation algorithm for
helicopter platforms was introduced in [6]. In this paper we
present the key aspects of the avoidance trajectory generation
algorithm for UA applications. The approximated dynamic
model of LOAM equipped UA platform for avoidance
trajectory generation purposes is derived by introducing the
following assumption:
The UA is modeled as a point-mass rigid body with three
linear degrees of freedom (3DOF);
The inertial reference system is centered on the initial
position of the UA point-mass, with the X axis pointing
eastward, the Y axis northward and the Z axis normal to
the ground;
The UA is subject to a constant gravitational acceleration
parallel and opposite to the Z axis, and for the purpose of
our estimation we assume

;
The mass of the vehicle is considered constant along the
avoidance trajectory;
During the avoidance maneuver, the load factor is set
close to the certified flight envelope limits of the UA. In
our case these correspond to


for the
pull-up maneuvers and


for the diving
maneuvers;
The airspeed of the UA is expressed as True Air Speed
(TAS). The wind is not explicitly expressed, although
implicitly considered in the vehicle-obstacle relative
motion. In our case, the assumed initial TAS is

.
The resulting system of differential equations for 3DOF
vehicle dynamics is:










(1)
where:
F
X
= sum of aerodynamic/propulsive forces along
X
axis;
= flight path angle;
= track angle;
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= bank angle.
We then assume that during the entire approach to the
obstacle, the vehicle control system can provide a linear
variation of

, up to the assumed maximum bank angle. This
can be expressed as:






(2)
The maximum roll rate was set at



. The
maximum bank angle was simply calculated as:



(3)
The algorithms for estimation of the obstacle absolute
motion based on differential geometry approach were
introduced in [12]. In order to provide the fast and reliable
performance required for our safety-critical task, the
avoidance trajectory generation is based on simplified
geometric shapes. In particular, given the different values of
uncertainty associated with the three cardinal directions, an
ellipsoidal avoidance volume is implemented in the algorithm.
The standard deviation of LOAM detection and tracking error
for each axis is given by:




(4)
In order to assure adequate safety levels, a separation buffer
is introduced, which inflates the ellipsoidal avoidance volume
associated with the obstacle. In particular, to provide a
confidence level of 95%, the uncertainty associated with the
position of an obstacle is calculated as twice the standard
deviation (i.e. the two-sigma) of the total obstacle detection
and tracking errors. When the distance between two detected
obstacles is comparable with the calculated uncertainty values,
or with the UA dimensions, the algorithm combines the two
obstacles in a single avoidance volume. The subsequent step
involves the selection of the optimal trajectory from the
generated set of safe trajectories, which is then fed to the
aircraft guidance subsystems. The implemented decision logic
is based on minimization of the following cost function:



(5)
where:

is the time at the minimum distance point to the
obstacle, hence it corresponds to the attainment of a safe
condition;



is the specific fuel consumption;

is the thrust profile;






is the
distance from the ellipsoidal avoidance volume of the
obstacle;

are the weightings attributed to time, fuel and
integral distance respectively.
In time-critical avoidance applications (i.e., closing-up
obstacles with high relative velocities and/or accelerations)
appropriate higher weightings are used for the time and
distance cost elements.
V.
S
IMULATION AND
R
ESULTS
Simulation activities were performed to validate the
avoidance trajectory generation algorithm and to assess its
performance. A realistic three-dimensional scenario for
obstacle avoidance is depicted in Fig. 9. The UA equipped
with LOAM is flying at an altitude z = 100 m Above Ground
Level (AGL) and approaching a power transmission line
consisting of a number of wires of 10mm in diameter. The
altitude of the lowest wire is 95m AGL and the altitude of the
highest wire is 115m AGL; the wires are separated by about
6.5m vertically and 5m laterally. The transmission line lies
approximately 70m in front of the UA. The original horizontal
flight trajectory would lead to a collision with the obstacle.
Fig. 9 Representation of LOAM system wire detection from a mini-
UA
After a successful detection of all the wires, the algorithm
calculates the distances among each of them. As previously
described the algorithm then recognizes that the calculated
distances are all comparable with the UA wingspan (3 meters)
and therefore combines all the wires in a single obstacle. The
centre C position and the semi-major axis a, b, c of the
resulting ellipsoidal avoidance volume are then calculated. In
particular C = {70m, 0m, 105m}; a = 15m; b = 100m; c =
25m. A representative set of avoidance trajectories generated
following these assumptions, is depicted in Fig. 10.
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Fig. 10 Valid and conflicting trajectories
in the UA reference system
Fig. 11
shows the separation envelopes between the UA and
the boundary surface of the ellipsoidal avoidance volume,
calculated
for each point of the UA conflicting and avoidance
trajectories.
Fig. 11
Absolute distance of the generated trajectories from the
ellips
oidal avoidance volume boundary
VI. LOAM
P
ERFORMANCE
E
VALUATION
The mathematical derivation of the various phenomena
affecting LOAM
laser beam in all the atmospheric conditions
were derived and discussed fo
r different applications in [7]
[11]. As detailed in [6], ground trials of
LOAM
performed
in order to verify the system detection performance
in various weather conditions, and to test the validity of the
mathematical models used for performance calculations.
was particularly important for preparing
LOAM
test activity. It is
in fact necessary to define a criteria for
determining the system detection range performances in the
worst environmental conditions, and with the worst obstacle
scenarios (i.e., small wires with low reflectivity), even without
in the UA reference system
shows the separation envelopes between the UA and
the boundary surface of the ellipsoidal avoidance volume,
for each point of the UA conflicting and avoidance
Absolute distance of the generated trajectories from the
oidal avoidance volume boundary
VALUATION
The mathematical derivation of the various phenomena
laser beam in all the atmospheric conditions
r different applications in [7]
-
LOAM
system were
in order to verify the system detection performance
in various weather conditions, and to test the validity of the
mathematical models used for performance calculations.
This
LOAM
system flight
in fact necessary to define a criteria for
determining the system detection range performances in the
worst environmental conditions, and with the worst obstacle
scenarios (i.e., small wires with low reflectivity), even without
performing real tests in t
experimental data collected in fear weather and with average
obstacles). Mathematical
modeling
LOAM system
detection performance
order to
derive the involved parameters and estim
LOAM system
detection performance
the flight testing. The
modeling
Analogously, the following
definitions are adopted
type obstacle
in line with
requirements:
Diameter: 5 mm
D
Shape: T
wisted or round
Reflection:
Purely diffuse (Lambertian)
Reflectivity: 20% (
θ
The reference environmental parameters
Visibility: V
800 m
Humidity: RH
100%
Temperature: T
50 °C
Rain:
Light/Medium/Heavy
Background: P
B
= 50 W/m
Ground trials of LOAM
system were performed in order to
verify the system detection performance in various weather
conditions, and to test the validity of the mathematical models
used for pe
rformance calculations.
important for preparing
LOAM
fact necessary to define a criteria for determining the system
detection range performances in the worst environmental
conditions, and with the wor
wires with low reflectivity), even without performing real tests
in these conditions (i.e., using experimental data collected in
fear weather and with average obstacles).
modeling
and ground testing of
were therefore required in order to give proper weights to the
parameters playing a role in realistic operational scenarios,
and to determine the target LOAM detection performances to
be demonstrated in flight.
involved.
Fig. 12 Detection per
formance models and ground test
As the ground test activities permitted to validate the
models developed, it was then possible to identify reference
sets of obstacle, background and atmospheric parameters
giving
the absolute minimum performance of
This is illustrated in Fig. 13
.
performing real tests in t
hese conditions (i.e., using
experimental data collected in fear weather and with average
modeling
and ground testing of
detection performance
are therefore required in
derive the involved parameters and estim
ate the target
detection performance
s to be demonstrated in
modeling
activities are detailed in [6].
definitions are adopted
for a wire
in line with
LOAM system operational
D
W
70 mm
wisted or round
Purely diffuse (Lambertian)
θ
= 0)
The reference environmental parameters
are the following:
800 m
100%
50 °C
Light/Medium/Heavy
= 50 W/m
2
srµm
system were performed in order to
verify the system detection performance in various weather
conditions, and to test the validity of the mathematical models
rformance calculations.
This was particularly
LOAM
flight test activity. It was in
fact necessary to define a criteria for determining the system
detection range performances in the worst environmental
conditions, and with the wor
st obstacle scenarios (i.e., small
wires with low reflectivity), even without performing real tests
in these conditions (i.e., using experimental data collected in
fear weather and with average obstacles).
Mathematical
and ground testing of
LOAM detection performance
were therefore required in order to give proper weights to the
parameters playing a role in realistic operational scenarios,
and to determine the target LOAM detection performances to
be demonstrated in flight.
Fig. 12 illustrates the process
formance models and ground test
As the ground test activities permitted to validate the
models developed, it was then possible to identify reference
sets of obstacle, background and atmospheric parameters
the absolute minimum performance of
LOAM system.
.
Obviously, the successive flight
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test activities were performed only in a small portion of
LOAM operational envelopes, but the results obtained could
be extended to the entire envelopes by using the validated
mathematical models.
Fig. 13 Minimum detection performance calculation
For initial design calculations, the wire obstacle detection
capability of LOAMis modelled by the following simplified
Signal to Noise Ratio (SNR) equation:
( )
NEPDRRP
deLLAE
SNR
D
W
R
rTrp
+
=
απ
ρ
γ
2
2
4
(6)
where:
E
P
= output laser pulse energy
A
r
= receiver aperture
L
T
= transmission losses (including beam shaping)
L
r
= reception losses (including optical filter)
γ = atmospheric extinction coefficient
d
W
= wire diameter
ρ = wire reflectivity
P
D
= pulse duration
R = obstacle range
α = beam divergence (l /e2)
D = initial beam diameter
NEP = noise equivalent power
The extinction coefficient (
γ
) is calculated as described in
[8], including the propagation in rainy conditions. This
approach (ESLM model) is particularly useful because it
provides a means of relating the atmospheric transmission of
the i
th
window to the atmospheric visibility, relative humidity
and rainfall-rate (i.e., readily measurable parameters). The first
assumption made [8] is that variations in the transmission are
caused by changes in the water content of the air. Specifically,
changes in the concentration of H
2
O cause changes in the
absorption, and changes in the size and number of water
droplets with humidity cause changes in the scattered
component. This is a valid assumption since the other
atmospheric constituents have a reasonably constant effect on
the transmittance of a given atmospheric window.
It is customary to express the number of H
2
O molecules
encountered by the beam of light in terms of the number of
precipitable millimeters of water in the path. Specifically, the
depth of the layer of water that would be formed if all the
water molecules along the propagation path were condensed in
a container having the same cross-sectional area as the beam is
the amount of precipitable water. A cubic meter of air having
an absolute humidity of
ρ
grams per m
3
would yield
condensed water that cover a l m
2
area and have a depth of:
ρ
3
10
=
w
(7)
where w' is the precipitable water having units of mm per
meter of path length. For a path length of z meters eq. 12
becomes:
zw =
ρ
3
10
(8)
where w is now the total precipitable water in millimeters. The
value of
ρ
, the density of water vapor, can be obtained using
the following equation [6], which is convenient for computer
code implementation:
( )
=
16.273
ln31.5
16.27322.25
8.1322
T
T
T
e
T
RH
ρ
(9)
where RH is the relative humidity (as a fraction), and T is the
absolute temperature (°K). Two empirical expressions,
developed by Langer [9], can be used to calculate the
absorptive transmittance
τ
ai
for the i
th
window for any given
value of the precipitable water content. These expressions are:
wA
ai
i
e
=
τ
, for
i
ww
<
(10)
i
w
w
k
i
iai
β
τ
=
, for
i
ww
>
(11)
where
A
i
, k
i
,
β
i
and
w
i
are constants whose values for each
atmospheric window are listed in [8]. For LOAM laser
wavelength (λ = 1550nm - 4
th
atmospheric window),
A
i
=
0.211,
k
i
= 0.802
,
β
i
= 0.111 and
w
i
= 1.1. In summary, (10)
and (11), together with (8) and (9), provide information that
can be used to obtain an estimate of the absorptive
transmittance (
τ
ai
) of laser beams having wavelengths that fall
within the various atmospheric windows. The results apply to
horizontal paths in the atmosphere near sea-level and for
varying relative humidity. To obtain the total atmospheric
transmittance we must multiply
τ
ai
by
τ
si
(i.e., the transmittance
due to scattering only).
Based on rigorous mathematical approaches, the scattering
properties of the atmosphere due to the aerosol particles are
difficult to quantify, and it is difficult to obtain an analytic
expression for the scattering coefficient that will yield
accurate values over a wide variety of conditions. However, an
empirical relationship that is often used to model the scattering
coefficient has the form:
(
)
4
21
+=
λλλβ
δ
CC
(12)
World Academy of Science, Engineering and Technology
International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering Vol:8 No:4, 2014
710
International Science Index Vol:8, No:4, 2014 waset.org/Publication/9997995
where
C
1
,
C
2
, and
δ
are constants determined by the aerosol
concentration and size distribution, and
λ
is the wavelength of
the radiation. The second term accounts for
Rayleigh
scattering. Since for all wavelengths longer than about 0.3
µm
the second term is considerably less than the first, it may be
neglected. It has been found that
3031 ..
±
δ
produces
reasonable results when applied to aerosols with a range of
particle sizes. An attempt has also been made to relate
δ
and
C
1
to the meteorological range. The apparent contrast
C
z
, of a
source when viewed at
λ =
0.55µm from a distance
z
is by
definition:
bz
bzsz
z
R
RR
C
=
(13)
where
R
sz
and
R
bz
are the apparent radiances of the source and
its background as seen from a distance
z
. For
µm 55.0
=
λ
,
the distance at which the ratio:
02.0
0
00
0
=
==
b
bs
bz
bzsz
z
R
RR
R
RR
C
C
V
(14)
is defined as the meteorological range
V
(or visual range). It
must be observed that this quantity is different from the
standard observer visibility (
V
obs
). Observer visibility is the
greatest distance at which it is just possible to see and identify
a target with the unaided eye. In daytime, the object used for
V
obs
measurements is dark against the horizon sky (e.g., high
contrast target), whereas during night time the target is a
moderately intense light source. The International Visibility
Code (IVC) is given in Table II.
TABLE
II
I
NTERNATIONAL
V
ISIBILITY
C
ODE
Designation Visibility
Dense Fog 0 – 50 m
Thick Fog 50 – 200 m
Moderate Fog 200 – 500 m
Light Fog 500 – 1 km
Thin Fog 1 – 2 km
Haze 2 – 4 km
Light Haze 4 – 10 km
Clear 10 – 20 km
Very Clear 20 – 50 km
Exceptionally Clear > 50 km
It is evident that, although the range of values for each
category is appropriate for general purposes, it is too broad for
scientific applications. Visibility is a subjective measurement
estimated by a trained observer and as such can have large
variability associated with the reported value. Variations are
created by observers having different threshold contrasts
looking at non-ideal targets. Obviously, visibility depends on
the aerosol distribution and it is very sensitive to the local
meteorological conditions. It is also dependent upon the view
angle with respect to the sun. As the sun angle approaches the
view angle, forward scattering into the line-of-sight increases
and the visibility decreases. Therefore, reports from local
weather stations may or may not represent the actual
conditions at which the experiment is taking place. Since
meteorological range is defined quantitatively using the
apparent contrast of a source (or the apparent radiances of the
source and its background) as seen from a certain distance, it
eliminates the subjective nature of the observer and the
distinction between day and night. Unfortunately, carelessness
has often resulted in using the term Visibility when
Meteorological Range is meant. To insure that there is no
confusion, Observer Visibility (
V
obs
) will be used in this thesis
to indicate that it is an estimate. If only
V
obs
is available, the
meteorological range (
V
) can be estimated [11] from:
(
)
obs
VV ±3.03.1 (15)
If we assume that the source radiance is much greater than
the background radiance (i.e., R
s
>>R
b
) and that the
background radiance is constant (i.e., R
bo
= R
bz
), then the
transmittance at
λ
= 0.55µm (where absorption is negligible)
is given by:
02.0
0
==
V
s
sv
e
R
R
β
(16)
Hence, we have:
91.3ln
0
==
V
R
R
s
sv
β
(17)
and also:
δ
λβ
==
1
91.3 C
V
(18)
It follows from (29) that the constant C
1
is given by:
δ
55.0
91.3
1
=
V
C (19)
With this result the transmittance at the centre of the i
th
window is:
z
V
si
i
e
=
δ
λ
τ
55.0
91.3
(20)
where λ
i
must be expressed in microns. If, because of haze, the
meteorological range is less than 6km, the exponent δ is
related to the meteorological range by the following empirical
formula:
3
585.0 V=
δ
(21)
where V is in kilometers. When V 6km, the exponent δ can
be calculated by:
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International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering Vol:8 No:4, 2014
711
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025.10057.0
+
=
V
δ
(22)
For exceptionally good visibility δ = 1.6, and for average
visibility δ 1.3. For LOAM, it is also very important to model
propagation through haze and precipitation. Haze refers to the
small particles suspended in the air. These particles consist of
microscopic salt crystals, very fine dust, and combustion
products. Their radii are less than 0.5µm. During periods of
high humidity, water molecules condense onto these particles,
which then increase in size. It is essential that these
condensation nuclei be available before condensation can take
place. Since salt is quite hygroscopic, it is by far the most
important condensation nucleus. Fog occurs when the
condensation nuclei grow into water droplets or ice crystals
with radii exceeding 0.5µm. Clouds are formed in the same
way; the only distinction between fog and clouds is that one
touches the ground whereas the other does not. By convention
fog limits the visibility to less than 1km, whereas in a mist the
visibility is greater than 1km. We know that in the early stages
of droplet growth the Mie attenuation factor K depends
strongly on the wavelength. When the drop has reached a
radius a 10 λ the value of K approaches 2, and the scattering
is now independent of wavelength, i.e., it is nonselective.
Since most of the fog droplets have radii ranging from 5 to
15µm they are comparable in size to the wavelength of
infrared radiation. Consequently the value of the scattering
cross section is near its maximum.It follows that the
transmission of fogs in either the visible or IR spectral region
is poor for any reasonable path length.This of course also
applies to clouds. Since haze particles are usually less than
0.5µm, we note that for laser beams in the IR spectral region
1<<
λ
a
and, therefore, scattering is not the dominating
attenuation mechanism. The scattering coefficient with rain,
however, depends strongly on the size of the drop and it can
be approximated by:
3
6
1025.1
a
tx
rain
=
β
(28)
where ∆x/∆t is the rainfall rate in centimeters of depth per
second and a is the radius of the drops in centimeters. Rainfall
rates for four different rain conditions and the corresponding
transmittance (due to scattering only) of a 1.8-km path are
shown in Table III [11].
TABLE
III
T
RANSMITTANCE OF A
1.8
KM
P
ATH
T
HROUGH
R
AIN
Rainfall (cm/h) Transmittance (1.8 km path)
0.25 0.88
1.25 0.74
2.5 0.65
10.0 0.38
These data are useful for order of magnitude estimates. In
order to obtain accurate estimates, the concentrations of the
different types of rain drops (radius) and the associated rainfall
rates should be known. In this case, the scattering coefficient
can be calculated as the sum of the partial coefficients
associated to the various rain drops.A simpler approach, used
in LOWTRAN, gives good approximations of the results
obtained for most concentrations of different rain particles.
Particularly, the scattering coefficient with rain has been
empirically related only to the rainfall rate
tx
(expressed
in mm/hour), as follows [10]:
63.0
365.0
t
x
rain
β
(29)
Table IV provides representative rainfall rates which can be
used when no direct measurements are available, to obtain
order of magnitude estimations of
rain
β
.
TABLE
IV
R
EPRESENTATIVE
R
AINFALL
R
ATES
Rain Intensity Rainfall (mm/hour)
Mist 0.025
Drizzle 0.25
Light 1.0
Moderate 4.0
Heavy 16
Thundershower 40
Cloud-burst 100
In the presence of rain, in addition to the scattering losses,
there are, of course, losses by absorption along the path, and
these must be included in the calculation of the total
atmospheric transmittance with rain.
In order to estimate the SNR from experimental LOAM
detector current measurements (i
SIG
), obtained with certain
obstacle ranges (R) and incidence angles (θ), SNR was
expressed as follows:
(
)
=
NOISE
SIG
i
Ri
SNR
θ
,
log20 (30)
The noise current terms in (2) was modeled as:
2222
RADKBKTHNOISE
iiiii +++=
(31)
where:
i
TH
= thermal noise current
i
BK
= background noise current
i
DK
= dark noise current
i
RA
= receiver amplifier noise
According to LOAM design characteristics, we have:
(
)
BkMMPqPi
AAhSBK
+= 22
(32)
L
k
BTH
R
BkT
Ki 4=
(33)
12
105.0
=
DK
i
(34)
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712
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12
105.1
=
RA
i
(35)
where:
P
S
= received solar power
P
h
= amplifier gain
M
A
= avalanche multiplier
k = noise factor of the avalanche photodiode
B = electronic bandwidth
K
B
= Boltzmann constant (1.39×10-23 J/°K)
T
k
= absolute temperature (°K)
R
L
= amplifier load resistance
For calculation purposes, the i
SIG
(R,
θ
) term was modeled as:
La
h
R
aWT
SIG
RK
P
R
eDdP
i1
4
3
2
3
=
λ
ηρ
γ
(36)
where:
P
T
= transmitted power
P
h
= amplifier gain
D
a
= aperture diameter
K
a
= aperture illumination constant =
(
)
4.5
sin
θ
The estimated range performances of LOAM for cable
obstacles having diameters (D
W
) of 5mm and 10mm, in
various visibility conditions and with all other parameters set
to the worst case are shown in Fig. 12.
Fig. 14 LOAM estimated detection range performance for wires
The false alarm probability is modeled as:
η
=
fa
fa
TB
P1
(37)
where:
B = receiver bandwidth
T
fa
= mean time between false alarms
η
= maximum useful/nonambiguous range
The mean time between false alarms corresponds to
electrical false alarms at the receiver level. The probability to
have several false alarms on a straight line pattern is much
lower. Statistically, these phenomena are described by the
False Alarm Rate (FAR) and detection probability (P
d
). If the
noise and signal distributions are known, the Signal-to-Noise
Ratio (SNR) can be estimated and the corresponding P
d
and
FAR can be determined. The average FAR of LOAM system
can be written as:
=
2
2
2
exp
32
1
n
t
I
I
FAR
τ
(38)
where:
τ = Electrical pulse length
I
t
= Threshold current
I
n
= Average noise current
LOAM P
d
is determined using pure Gaussian statistics:
=
n
n
n
n
I
II
d
I
i
d
I
i
P
n
nt
2
2
exp
1
2
2
2
π
(39)
I
n
= average signal current
i
n
= instantaneous noise current
The false alarm probability (P
fa
) is given by:
FARP
fa
=
τ
(40)
and the cumulative detection probability (P
D
) is given by:
( )
=
= m
i
iM
d
i
d
i
MD PPCP
0
11
(41)
where:
M = number of possible detections
m = minimum number of detections required
The scenario in which ground tests were performed is
shown in Fig. 15.
Fig. 15 LOAM ground tests scenario
A comparison between the SNR predicted (SNR
P
) with
γ
calculated using the ESLM model (0.19km
-1
γ
0.22km
-1
for
clear weather and 1.23km
-1
γ
2.94km
-1
for rainy conditions),
assuming a background power of 10 Watt/m
2
/sr/µm and
ρ
=
0.5, and estimated from experimental data (SNR
E
), is shown in
Table V.
World Academy of Science, Engineering and Technology
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713
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TABLE
V
LOAM
P
REDICTED AND
M
EASU
RED
Clear Weather
V=10km V=12.5km
V=15km Light
SNR
P
4.90
×
10
4
4.95
×
10
4
5.02
×
10
4
3.14
×
10
SNR
E
3.35
×
10
4
3.80
×
10
4
4.27
×
10
4
2.87
×
10
VII.
F
LIGHT
T
EST
A
CTIVITIES
Figs. 16 and 17 show LOAM
prototype used for the
helicopter flight trials. Particularly,
LOAM
shown in Fig. 16 and
the pilot interface units are shown in Fig.
17.
Fig. 16 Sub-units of LOAM
prototype
Fig. 17 Prototype p
ilot interface units and
Two different test-
bed platforms were used for the trials:
the NH-300 and AB-
212 helicopters.
LOAMSHU mounted on the AB-212.
Fig. 18
LOAM installed on AB
RED
SNR
Rain
Medium Heavy
10
4
1.83×104
1.45×104
10
4
2.47×104
2.13×104
CTIVITIES
prototype used for the
LOAM
sub-units are
the pilot interface units are shown in Fig.
prototype
ilot interface units and
SHU assembly
bed platforms were used for the trials:
212 helicopters.
Fig. 18 shows
LOAM installed on AB
-212
As shown in Fig. 19, t
he Cockpit Display Unit (CDU) used
for the trials
was installed at the centre of the AB
shield
in order to be accessible to both pilot and co
the AB-212 test campaign,
LOAM
was installed in the centre of the middle
accessible to both pilot and co
During the test flights, a Flight Test Engineer also operated a
computer, linked to LOAM
system and displaying in real
a 3-
dimensional image reconstructed using
images were also
recorded for
results of this test campaign were satisfactory.
performances were in
accordance with the
and LOAM
detection/classification data processing algorithms
were validated.
Furthermore, it was verified that
History Function was correctly implemented.
Fig. 19 Display unit
installed
Fig. 20
LOAM Main Control Unit on AB
More detailed results of
activities performed on
various
are presented in [6] and [8].
VIII.
C
ONCLUSIONS AND
The challenges
and opportunities asso
introduction of laser-based
obstacle warning and avoidance
systems in
Unmanned Aircraft (UA)
paper. Specifically, we
have addressed
and software
design tailoring of an existing system
MCU
he Cockpit Display Unit (CDU) used
was installed at the centre of the AB
-212 glare
in order to be accessible to both pilot and co
-pilot.For
LOAM
Main Control Unit (MCU)
was installed in the centre of the middle
-consolein a position
accessible to both pilot and co
-pilot, as shown in Fig. 20.
During the test flights, a Flight Test Engineer also operated a
system and displaying in real
-time
dimensional image reconstructed using
LOAM data. All
recorded for
post-flight data analysis. The
results of this test campaign were satisfactory.
LOAM range
accordance with the
model predictions
detection/classification data processing algorithms
Furthermore, it was verified that
LOAM
History Function was correctly implemented.
installed
on AB-212 glareshield
LOAM Main Control Unit on AB
-212
More detailed results of
LOAM ground and flight test
various
dedicated test-bed platforms
ONCLUSIONS AND
F
UTURE
W
ORK
and opportunities asso
ciated with the
obstacle warning and avoidance
Unmanned Aircraft (UA)
are discussed in this
have addressed
the potential hardware
design tailoring of an existing system
: The Laser
World Academy of Science, Engineering and Technology
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714
International Science Index Vol:8, No:4, 2014 waset.org/Publication/9997995
Obstacle Avoidance Marconi (LOAM) developed and tested
by SELEX-ES and the Italian Air Force Experimental Flight
Test Centre. In particular, some preliminary mathematical
models for UA flight dynamics and avoidance trajectory
generation have been described. The original system design,
including the requirements, the architecture, the components,
the algorithms is summarized together with the ground testing
and flight testing activities performed. Future ground and
flight tests will be performed in order to assess the
performance of LOAM system integrated on the various other
flying platforms in day/night with various
weather/environmental conditions and to optimize the ground
operator Human Machine Interface (HMI) design. For future
UA test flights, a dedicated control unit is also being designed
to be integrated in the ground-based pilot station. Its
characteristics are similar to the MCU developed and
presented in [6]. However, as this MCU has to be operated by
the ground UA pilot, in this case LOAM operating modes are
activated using two different communication data links for
Line-of-Sight (LOS) and Beyond LOS operations.
Additionally, LOAM display functions will be fully integrated
in the UA remote control position and the required LOAM
display formats displayed to the UA pilot in real-time. The
potential interfaces between LOAM and other on-
board/ground systems are also being investigated. The system
was previously implemented in a stand-alone setup [8]. It is
expected that for the high-dynamics UA applications, an
extensive integration with the developed Sense-and-Avoid
(SAA) system, described in [12], and with the low-cost multi-
sensor UA navigation and guidance system including Vision
Based Navigation (VBN) described in [13] and [14] will be
fundamental. In order to attain unrestricted access to airspace,
the UA will have to incorporate the SAA capabilities in a
comprehensive avionics architecture that should also include
the emerging Communication, Navigation, Surveillance, Air
Traffic Management (CNS/ATM) systems. Current avionics
and CNS/ATM research is therefore addressing the possible
direct integration of LOAM and other avoidance systems with
manned aircraft and UA Flight Controls/Autopilots and Flight
Management Systems [15], [16], and with the next generation
of ground-based Air Traffic Management (ATM) systems
being conceived for 4-Dimensional Trajectory Planning,
Negotiation and Validation (4-PNV) in line with the
requirements set by the SESAR (Single European Sky ATM
Research) and NextGen (Next Generation Air Transportation
System) research initiatives in Europe and in the United States
[17], [18].
A
CKNOWLEDGMENT
The originaldevelopment and testing activity was funded by
the Italian Ministry of Defence (MoD) under R&D contract
No. 2097-22-12-2000. The authors wish to thank the
personnel of SELEX-ES, LOT-ORIEL, the Italian Air Force
Experimental Flight test Centre, and the Italian MoD Laser
Test Range (PILASTER) for helping in the preparation and
execution of the ground and flight test activities.
R
EFERENCES
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International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering Vol:8 No:4, 2014
715
International Science Index Vol:8, No:4, 2014 waset.org/Publication/9997995
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The innovative concept and the trajectory computation algorithms of a novel Flight Management System (FMS) are presented. The Next Generation Flight Management System (NG-FMS) is developed for Four Dimensional (4D) Intent Based Operations (IBO) adhered to the requirements set by the Single European Sky Air Traffic Management Research (SESAR) and the Clean Sky Joint Technological Initiative (JTI) for Aeronautics and Air Transport. The NG-FMS provides advanced functionalities that are fully interoperable with future ground based 4DT Planning, Negotiation and Validation (4-PNV) systems, enabling automated Intent Based Operations (IBO) in the future Air Traffic Management (ATM) context. A family of 4DT intents is generated by the NG-FMS corresponding to a set of performance weightings agreed between Air Navigation Service Providers (ANSP) and Airline Operation Centres (AOC). Both pseudospectral and weighted-sum methods are adopted to achieve trajectory optimisation in quasi real-time for a variety of operational tasks. The uncertainties affecting the nominal trajectories are investigated to obtain the total Guidance, Navigation and Control (GNC) system error budget. These budgets are compared with the Required Navigation Performance (RNP) values set by International Civil Aviation Organization (ICAO) for the various aircraft operational flight tasks considered.