PROPAGATION IN THE TURBULENT MARINE SURFACE LAYER AND
IMPLICATIONS FOR SENSOR SYSTEMS
A. S. Kulessa, H. J. Hansen and W. Marwood
EWRD, DSTO
P.O. Box 1500
Edinburgh, SA, 5111
ABSTRACT
Monin-Obukhov theory provides a model for investigating
the propagation of RF emissions in evaporation ducts that
are a common feature in the maritime environment.
Experimental results for propagation studies that have been
undertaken by DSTO are presented, together with a
statistical analysis and interpretation of some of these
results. The Split-Step Parabolic Equation is used to show
the significance of the surface duct, particularly as the
emitter frequency moves upwards from the microwave to
the millimeter wave domain.
Index Terms — Parabolic Equation, propagation, marine
boundary layer, evaporation ducts, modeling.
1.
INTRODUCTION
Theoretical arguments are presented that explain the
prevalence of non-standard radio-refractive index profiles
in the marine surface layer. The discussion shows that
evaporation ducting dominates thermally unstable and
neutral surface layers and that the strength of the ducting is
partly linked to atmospheric turbulence [1]. This is
certainly the case provided the turbulence is not too strong.
The presence of turbulence in the surface layer creates a
stochastic evaporation duct with ducting parameters
varying in time and space. Monte-Carlo simulations that
solve the parabolic equation over many realizations show
that the mean, variance and skewness of received signal
amplitude distributions are dependent on the proximity of a
given receiving antenna to a multi-path null. These
variations may degrade the performance of conventional
electronic support (ES) systems. The use of multichannel
techniques can be shown to not only overcome the effects
of multipath nulls, but also to allow accurate sampling of
the diffraction pattern for a given signal. A model is
developed in this paper for the extended refractivity profile
through which a signal propagates. Current work is then
discussed which is associated with the development of a
multichannel receiver that will be used to experimentally
validate matched field processing techniques for signal
source localisation in atmospheres that range from
'standard' to anomalous.
2.
PROPAGATION IN THE MARINE SURFACE LAYER
Propagation in clear-air conditions in a maritime
environment where the atmosphere is horizontally stratified
can be ‘standard’ or ‘anomalous’ depending on the
refractivity profile. The 'standard radio atmosphere' is a
term that is often used by radio or communication engineers
to describe a refractivity profile that, close to the earth’s
surface, is well approximated by a straight line given by the
equation
(1)
nz   n0  3.9 x10 8 z
where z is the height in metres. In this equation the term, n
refers to the refractive index of the atmosphere which is
generally a complex quantity. The real part of n influences
the bending or refraction of radiowaves while the imaginary
part affects the absorption or attenuation of radiowaves.
‘Anomalous’ propagation is defined to be propagation of
radiowaves through an atmosphere with a non-standard
refractive index profile. The refractive index depends on
three quantities: atmospheric water vapour pressure,
temperature and pressure. In a standard atmosphere,
vertical profiles of these quantities are well defined and one
may well state that 'anomalous propagation' is as anomalous
as non-standard profiles of water vapour pressure,
atmospheric temperature and pressure. This rephrasing of
the description of anomalous propagation leads to another
question which can more readily be addressed: how
anomalous are non-standard profiles of water vapour
pressure, atmospheric temperature and atmospheric
pressure? The following sections provide some answers to
this question.
Profiles of the relevant atmospheric quantities are derived
from solutions of the equations that govern the flow and
thermodynamics of the atmosphere, or more specifically,
the atmospheric boundary layer. These equations are the
equations of continuity (conservation of mass),
thermodynamics (conservation of enthalpy), humidity
(conservation of water vapour), the three Navier-Stokes
equations (conservation of momentum) as well as the
equation of state. Normally one would adopt a numerical
approach to solving this set of equations but where
turbulence is important, especially in the lower regions of
the atmosphere, numeric schemes become impracticable
due to the fine mesh that must be used to resolve the full
spectrum of turbulence. Because such a deterministic
approach is untenable, a statistical approach should be
considered.
Often the dimensionless variable z/L is denoted by the
symbol . Associated with  are self–similar stability
functions  that take the form
The statistical method for addressing atmospheric
turbulence involves representing atmospheric quantities as
the sum of a mean and a random fluctuating component. If
velocity, temperature and humidity are represented in this
way, a simplified version of the governing equations can be
stated in which average flow fields are incorporated.
However, the issue in solving these statistical equations is
that the set of equations is not closed due to the presence of
second order moments or co-variance terms which result in
too many unknowns for the number of equations. A
successful way to reduce the number of unknowns is to
parameterize the higher order moments in terms of the
lower order moments and ‘local closure’ is a first order
closure scheme that is used when the highest order of
moments is of second order.
The calculation of refractivity profiles requires
characterisation of flow in atmospheric surface layers using
four variables ( u* , z, g /  v and the covariance term
Local closure postulates that the covariance of a particular
quantity at a particular location is related by some
coefficient term to the mean vertical gradient of that
quantity at that location. This statement can be expressed
by the following equation:
 i ' w'   K i
 i
z
(2)
In equation (2), w is the vertical component of wind speed,
is the ith atmospheric quantity of interest and the
coefficient terms Ki are assigned varying names in the
literature. It is referred to the eddy viscosity when is the
transverse wind otherwise it is called the eddy diffusivity or
eddy diffusivity of water vapour.
3.
REFRACTIVE INDEX WITHIN THE SURFACE LAYER
The eddy coefficients Ki are significant because they
provide the parameters for modeling the refractive index in
thermally unstable surface layer regions where ducting
conditions arise. The Ki coefficients are not, in general,
constant terms and within the surface layer they take the
form
z
K i  u* kz i1  
L
(3)
where the variable u* denotes the friction velocity within
the surface layer, z denotes height above the sea surface
and L is a scale length termed the Obukhov Length and can
be thought of as a stability parameter.
For thermally unstable conditions, the Obukhov Length is
negative and positive for a thermally stable atmosphere.
  a(1  b ) c
(4)
where a, b and c are constants.
 v ' w' ) with a stability function of  This is termed the
Monin-Obukhov similarity requirement. The choice of the
stability function provides options. However, once chosen,
the mean profiles of humidity and temperature are derived
from integration. The pressure profile then follows from
hydrostatic equilibrium arguments which, together with the
humidity and temperature profiles, permit refractive index
profiles to be calculated.
This formalism is powerful because when the Obukhov
parameter is negative it relates to the thermally unstable air
which is typical of the marine environment around most of
the Australian coastline during most of the year and is
especially valid in tropical regions when winds are present.
The magnitude of the Obukhov parameter denotes the
depth of the surface layer and as the wind speed decreases,
|L| and u* tend to zero. When there is no wind the MoninObukhov theory is invalid and an alternative similarity
based on free convection is more appropriate.
Over the sea, for regimes where Monin-Obukhov theory is
valid, the resulting refractive index profile is never
'standard'. Furthermore, the resulting refractive index
profile is a ducting profile and is referred to as the
evaporation duct. Over a certain range of wind speed, the
duct height increases as the wind speed increases. Keeping
other factors constant, the scaling length, L depends on the
friction velocity, in such a way that as wind speeds
increases, |L| increases and in the limiting case,  tends to
zero and a neutral atmosphere results. Buoyancy is
negligible and forced convection dominates.
4.
PLOTTING REFRACTIVITY PROFIILES
As changes in the air’s refractive index are very small (as
seen from equation (1), a better way to visualise the
refractive index n is to transform it to the modified
refractivity, M. This transformation is represented by
M ( z )  106 m( z )  1 where m( z )  n 
z
where
ae
ae is the mean radius of the earth in km.
The other advantage of the transformation is in visualising
ducts. When M is plotted against height z, a negative slope
indicates a ducting profile. M profiles are plotted in Figure
1 for different wind speeds.
length over water that are valid if the atmosphere is indeed
neutral.
50
Experiments that have been conducted in the past show that
in the presence of winds over the sea surface and in coastal
regions, the atmosphere is often thermally unstable. This
means that the processes described in the previous section
should be used to determine the M profile.
Height (metres)
40
30
20
4.2 The ’Near-neutral’ atmosphere
10
0
350
360
370
380
390
400
410
Modified Refractivity M
Figure 1 Modified Refractivity profiles for wind speeds of 3 m/s,
7m/s and 12 m/s. All other factors kept constant. Sea surface
temperature is set to 26 deg C. Relative humidity at 23 m is 64%,
temperature is 24.5 deg C. Duct heights vary from about 9 to 20
m.
4.1 The neutral atmosphere
For the neutral atmosphere, first order closure schemes are
appropriate in which fluxes of transverse wind, water
vapour pressure and latent heat relate to the mean gradients
of transverse wind speed, specific humidity and virtual
potential temperature. The fluxes can either be directly
measured or bulk transfer coefficients can be computed and
flux values inferred. A self-similar stability function
describes the relation between the fluxes and the mean
gradients.
For thermally neutral conditions, the stability function is
simply a constant equal to unity. In this special case, the
gradients of specific humidity and virtual potential
temperature when combined with a hydrostatic pressure
gradient yield an analytical formulation for the modified
refractivity. In this case, the modified refractivity is given
as a function of height
 z  z0
1
M ( z )  M (0)   z  d  z 0  ln 

8
 z0




(5)
Comparison of evaporation duct models based on thermally
unstable and neutral atmospheres of equal duct height show
that the profile gradients are similar at heights ranging from
about 2 m to the duct height, typically between about 5 and
25 m in tropical Australian waters [2].
Because major discrepancies arise only near the surface,
i.e. the first two metres, an approximation of the M-profile
in a “near–neutral” unstable atmosphere can be given as
 z  z0
1
M z   M 0   z  d  z 0  ln 

8
 z0

  f


(6)
where the constant f is treated as a free parameter. It is
obvious from equation (6) that both d and f can be
estimated by a linear least squares process, given
measurements of M for heights at or above about 2 meters.
It must also be pointed out that introducing the constant f
does not alter the propagation effects as it is the gradient of
the refractive index profile that affects radio-wave
propagation.
The value of equations (5) and (6) lies in the fact that the
M-profiles are simply parameterized by the duct height.
Iterative algorithms for determining the M-profiles are
avoided. The next section explains why a more accurate
physical model is exchanged for mathematical ease of
computation.
5. MONTE-CARLO SIMULATUIONS OF PROPAGATION
THROUGH A TURBULENT ATMOSPHERE
This profile describes an evaporation duct modified
refractivity where the parameter z 0 is the roughness length
for momentum, and d is the duct height. Repeated
measurements of temperature, humidity and pressure at
fixed heights mounted along a spar have been carried out
during several experimental campaigns [2]. The
corresponding modified refractivity data can be used to
estimate the unknown parameters d and z 0 using the
above equations.
Conducting experiments which involve the deployment of a
spar buoy allows continuous measurements of duct height
to be taken over periods of days. The slowly varying
(diurnal) changes in the mean duct height can be filtered
out, leaving a time series of duct height fluctuations d ' . A
probability density function f (d ' ) can then be constructed
by ‘binning’ and normalizing these fluctuations. Such a
probability density can then be used to sample duct height
fluctuations in a Monte-Carlo experiment designed to
simulate propagation through a rapidly time and space
varying evaporation duct.
This approach is at times ill-conditioned and it is better to
specify the roughness length first and treat the duct height
d as the only free parameter. This is because there are
empirical relations between wind speed and roughness
A coherence length can be determined by using two
instrumented buoys and collecting data at various
separation distances for a specified sampling time. A
correlation function can then be constructed from the cross-
correlation of the two duct height time series at the various
separation distances. The coherence length can be defined
as the separation distance that corresponds to a specified
low value of the cross-correlation coefficient. This
coherence length defines the points along the propagation
path where a new duct height should be sampled.
For a given propagation path, the length of which is
represented by D  Xn , the duct height along the
propagation path is therefore given as
d x  
n 1
 H ( x  Xi   H x  X i  1(d  d i ' )
(7)
i 1
where H ( x  X ) is the Heaviside step function, d is the
mean duct height along the propagation path, X is the
coherence length and d ' is random variable which is a
measure of the fluctuation of duct height. The probability
that d ' has a value between a and b is given as
Pr a  d '  b  
b
 f ( )d
(8)
a
We now have a modified refractivity function which
depends both upon the propagation path and the duct
height. It is a stochastic function and the jth realization is
given by
 z  z0 
1

(9)
M j x, z   f  z  (d j x   z 0 ln 

8
 z0 


Here the surface refractivity is absorbed into the mean
constant f . Statistics of a field component  can be
obtained by solving many realizations of the parabolic
approximation of the Helmholtz equation. In cylindrical
coordinates, the jth realization is given as
2


z 6
2



 j

2
ik

k
M
(
x
,
z
)

10

1
 j

z 2
x
ae



 2 j
 j
2
(10)
where
ae is the mean radius of the earth in km.
Equation (10) can be solved numerically using the Fourier
Split-Step algorithm. The algorithm allows solutions to be
determined
at
discrete
heights
separated
by z simultaneously for a given range x along the
propagation path. Thus the solution is achieved in steps
along the propagation path x  x .
6.
AMPLITUDE DISTRIBUTION ALONG A FIXED 12 GHZ
PROPAGATION PATH
The distributions of the amplitudes of signals which have
propagated through the marine surface layer have been
calculated from experimental measurements taken from
instrumented spar buoys and compared with signal
amplitude distributions derived from Monte-Carlo
experiments. In one experiment in a tropical environment,
signal amplitude data was collected over an 18km
propagation path located between Orpheus Island and
Lucinda Jetty, North Queensland. The propagation link
consisted of a CW 12GHz emitter mounted at an average
height of six metres above sea level and located in the
vicinity of the JCU Research Station at Orpheus Island. The
signal amplitude was measured, after propagating 18km, at
Lucinda Jetty at discrete heights between 10 and 23 meters
above mean sea level. The experiment was conducted at the
end of the dry season at a time when measurements of the
evaporation duct height were found to vary between about
10 and 23 m. The duct height measurements were obtained
using a spar buoy instrumented with PT100 temperature
sensors and Vaisala HMP35A humidity probes and
pressure sensors. Statistics for the duct heights were
calculated from these measurements. The results of the
experiments and the associated simulations are shown in
Figure 2. The shape of the histograms derived from Monte
Carlo simulation is in general agreement with the measured
data. Solid lines represent the results from simulation and
the dotted lines represent the experimental results.
Overall the signal amplitude drops and the variance
increases as the receiver height comes close to a multi-path
null, and for strong signals with low variance very little
skewness is evident. The solid curves in the graphs are the
amplitude histograms as determined by the Mont-Carlo
simulations.
7.
AMPLITUDE DISTRIBUTION ALONG A FIXED MMW
PROPAGATION PATH
A radio link experiment has also been conducted in ducting
conditions when both evaporation on the sea surface and
surface layer winds were present. Consequently, a
comparison of the distribution of received signal
amplitudes with the Monte Carlo simulations where the
 0 Monin-Obukhov similarity has been invoked has been
conducted.
Signal amplitude data were collected over a 10km
propagation path located between Weeroona Island (Port
Flinders) and Port Germein, South Australia. The
propagation link featured several narrow beamwidth Ka
band emitters mounted at heights of 8.82 and 3.01 and 2.4
meters above sea level, transmitting at a low power. Signal
amplitudes were measured at the Port Germein end at
heights of 2.2, 2.8, 3.8 and 6.1 m above mean sea level.
Unlike the open ocean conditions experienced off the coast
of Lucinda, QLD, the marine atmosphere in the northern
Spencer Gulf is strongly affected by the surrounding land
mass and it is evident that advective processes played a
large part in influencing the refractive index structure.
Some results from this experiment are shown in Figure 3
which shows received power level histograms from four
receivers at discrete heights for three different Ka band
emitters operating at three different heights.
It was estimated from aircraft measurements of atmospheric
conditions that the evaporation duct height was less than 10
m during the time that this RF data was collected. The red
histograms show the received power levels of the 2.4m high
emitter at heights of 2.2, 2.8, 3.8 and 6.1 m (shown from
top to bottom respectively in the figure). Note both that
there was a very large drop in the mean received power
level at 6.1 m and also that there was a much higher
variance in the received power level at this height. The
power level distributions are consistent with a mean duct
height of 9.5 m. In the mean, a null is indeed observed at a
height of 6.1 m. However, some caution must be used when
interpreting these results. The radio link was operated with
receivers (and also two transmitters) positioned close to the
sea surface at a time when the atmosphere was not neutral.
The model for the evaporation duct outlined above would
not be a good approximation for the actual evaporation
duct. In this case another model must be used but the
details are beyond the scope of this paper. The results are
presented nevertheless to show the variation in mean,
variance and skewness of the power level distributions at
Ka band. The received power level distributions of the two
other emitters do not exhibit such large variations because
they are not near any nulls.
Figure 2 Top left is shown the received signal level as a function of height for a fixed propagation link operating at 12 GHz. The dotted curve
in the top right hand graph is a histogram of received power levels for a receiving height of 23 m. The bottom left hand graph shows the
histogram of received power levels (dotted curve) for the next height down, i.e. 21 m. The bottom right graph shows the distribution for the
receiver when it is at a height of 11 m.
8.
IMPLICATION FOR THE DETECTION OF SHF AND
EHF SIGNALS OVER THE SEA SURFACE
Maritime Electronic Warfare is concerned with the
interception of signals covering the entire radio spectrum.
Signals may be either narrow or broadband and exhibit a
multitude of different waveforms. Depending on the nature
of maritime operations, transmissions may be continuous,
bursty or may be characterised by their short duration. In
EW operations, often the goal is to intercept threat
emissions and then track the threat platform. For Electronic
Surveillance (ES) systems, early signal detection can imply
platform detection at greater distances and hence one of the
major challenges in ES sensor design is to achieve the
highest sensitivity.
Improved detection range, high accuracy Angle of Arrival
(AOA) determination and wide angle instantaneous
coverage are features that will characterize next-generation
Electronic Surveillance sensors. Additional information
will also be required regarding signal elevation. One
approach for the accurate measurement of these signal
parameters is the use of distributed multi-element antennas.
Each multi-element antenna element can be relatively
small, and large platforms such as navy surface ships can
accommodate a network of such antennas distributed both
horizontally and vertically.
Appropriate signal processing techniques for distributed
multi-channel antennas will provide the desired
simultaneous frequency and spatial coverage as well as very
accurate angle-of-arrival measurement capability. An
aspect of this approach to RF surveillance is that multielement arrays of the type described make it possible to
optimally copy emissions of interest, whether they are from
radars, communications or broadcast emitters. Further
advantages of the approach is that interference from the
host platform or from Own-Force Emitters can be
minimized, and the spurious signals generated by internal
non-linearities can also be suppressed. As signal densities
increase and so make the de-interleaving task more
difficult, a further possibility is to adapt optimal signal copy
techniques and track emitters directly rather than
synthesizing tracks from databased signal parameters.
With an increasing emphasis on the improvement of sensor
parameter measurement such as sensitivity and bearing
resolution, it is vitally important to address the question of
whether the atmosphere imposes physical limits on the
measurement of these parameters. In general, it is necessary
to consider relevant propagation mechanisms and
investigate their effects on incoming signals and then
possibly to compensate for any affects that may degrade the
performance of own-force surveillance sensors.
Figure 3 shows received power level histograms for three different Ka band emitters operating at three different heights. Three sets of
receivers are positioned at four heights. The combination of transmitter and receiver heights for the red link shows a drop in mean signal level
and a broad variance as the receiver is in this case close to null. The signal is strong and variance low when the receiver is aligned in the
middle of an interference lobe. For the blue and black links, the combination of emitters and receivers was such that reception was never near
a multi-path null
Figure 4 Calculated field intensities to a height of 400m and a
range of 40Km for a 2 GHz source at 6m. The duct profile is
shown in Figure 5 and has a height of 45m and a standard
variation of 3m.
Figure 5 Calculated final field intensity at 40km for a 2 GHz
source at 6m. The duct has a height of 40m and its profile is
shown in red. The duct height of 45m has a standard variation of
3m.
To investigate these questions for environments which are
characterized by evaporation ducts, and which are prevalent
in areas of interest, simulations have been carried out using
representative evaporation duct refractivity profiles. The
range chosen for the simulations was 40 km as this is a
typical range over which maritime navigational information
is required.
To better represent a real world propagation environment, a
3m variance in duct height over the 40km path length was
considered, although few differences were noted between
such rough ducts and the smooth versions that are often
used in the literature. In figures 4-9 the electromagnetic
field from a source at 6m, in a surface duct of height 40m,
is computed using the Split-Step Parabolic Equation [3].
The frequencies chosen for illustration are 2 GHz, 10 GHz,
35 GHz and finally 94 GHz. At 2 GHz the simulation
revealed no trapping of energy. The range over which the
signal strength varied was between 6 and 20 dB below the
level at the energy peak.
Figure 8 Calculated field intensities to a height of 400m and a
range of 40Km for a 35 GHz source at 6m. The duct profile is as
shown in Figure 9. The duct height of 45m has a standard
variation of 3m.
Figure 6 Calculated field intensities to a height of 400m and a
range of 40Km for a 10 GHz source at 6m. The duct profile is
shown in Figure 7. The duct height of 45m has a standard
variation of 3m.
Figure 9 Calculated final field intensity at 40km for a 35 GHz
source at 6m. The duct height is 45m and its profile is shown in
red. The duct height of 45m has a standard variation of 3m.
Figure 7 Calculated final field intensity at 40km for a 10 GHz
source at 6m. The duct height is 40m and its profile is shown in
red. The duct height of 45m has a standard variation of 3m.
At 10 GHz it is evident that there is some trapping of signal
energy within the duct and the plot exhibits a more complex
interference structure as a result of the shorter wavelength.
At 35 GHz, there is substantial trapping of energy. Indeed
most of the energy that propagates lies within the duct.
Further, signal strengths above ~20m lie over 20 dB below
the levels observed at the transmitter height.
A final set of simulations was carried out at 94 GHz, a
frequency that lies in the next atmospheric 'window' above
30-40 GHz for RF propagation. Figure 10 represents a
'free-space' propagation model for reference. It shows the
computed field intensities that would be observed in an
environment where the refractive index was that of free
space. The 'lobing' structure is uniform and is a function of
the height of the transmitter from the surface. Figure 12
shows the consequence of adding a 'standard atmosphere' to
the propagation environment. The model shows a
substantial confinement of energy even without the
presence of a duct. Although the model has been found to
be self-consistent, this is a result that requires verification.
When the same 45m duct and 6m source height that has
been considered at the lower frequencies is used at 94 GHz,
very strong ducting is evident.
option is the use of a distributed array, although the
frequency bandwidth of the emitter can be used to partially
compensate for limitations in the number of array elements
or the array aperture.
Figure 10 Calculated field intensities for a 94 GHz source at 6m
in an artificial 'free-space' environment.
Figure 12 Calculated field intensities to a height of 200m and a
range of 90Km for a 94 GHz source at 6m. The duct profile is
shown in Figure 7. The duct height of 45m has a standard
variation of 3m.
Figure 11 Calculated field intensities to a height of 200m and a
range of 90 Km for a 94 GHz source at 6m in a 'standard
atmosphere'.
The results are shown in Figure 12 and show that the field
strength at 90 Km is substantially greater than the field
strength in the non-ducting environment Figure 13 shows
the final field strength for this simulation at 95 Km and the
associated ducting profile.
For a maritime platform with a single high (>30m) antenna
in an environment characterized by evaporation ducts and
where low-placed mmW signals may be present it is clear
that the sensor placement is not well matched to the
propagation environment. A better approach for future
surveillance sensors may well be a distributed array of
sensors where at least some of the sensors will always be in
a position to sense the incident energy. Such an array would
lend itself well to the investigation of matched field
processing for source localisation [4]. For such a technique
to work, knowledge of the propagation environment is
critical. A number of authors have proposed refractivity
profile estimation via own-ship measurements, GPS signals
and radar clutter [5].
Field measurements proposals have ranged from a
simulated 50 element array, to a single receiver but with an
extensive set of measured frequencies. In practice, the only
Figure 13 Calculated final field intensity at 95km for a 94 GHz
source at 6m. The duct height is 45m and its profile is shown in
red. The duct height of 45m has a standard variation of 3m.
DSTO is currently undertaking the construction of a
multichannel array that will allow the validation of matched
field techniques for source localisation, as well as
developing array-based techniques for ES systems.
9.
CONCLUSION
The refractivity profile in the maritime emvironment is a
complex function of time. To date DSTO has carried out
limited experiments to determine surface evaporation duct
height correlation lengths present in varying wind
conditions, and has done limited work on the impact of the
duct height statistics on behaviour of the E-M fields that
propagate through surface ducts.
From theoretical arguments it is shown that in clear air
conditions anomalous propagation in the marine surface
layer is not anomalous but is the norm. It is not a 'standard'
refractive index profile that prevails over tropical waters
but an evaporative duct profile. Evaporation ducts are
sustained under light to strong breezes but as the wind
speed increases, and hence the turbulence of the air,
received signal power varies randomly and this randomness
can be modelled by a stochastic evaporation duct where the
duct height fluctuates randomly across the propagation
path. Calculations show that the mean, variance and
skewness of received power levels are affected by how
close a receiving antenna is positioned to a multi-path null
and there is some experiemntal validation for these
calcualtions from X band and Ka band measurements. Such
variations can imply system outages if the receiver is
detecting low power signals close to the minimum
detectable signal level.
On the other hand, the ducting of microwave and millimetre
wave emissions imply that strong signal levels are detected
when receiving antennas are positioned closer to the sea
surface. For microwave emission, the first interference lobe
is concentrated close to the sea surface and the interference
associated pattern extends the range over which signals can
be detected, so enhancing the range of ESM receivers. Of
particular interest are the results for 94 GHz. Currently
there are few emitters in the maritime environment that use
such frequencies, but this study has indicated that there are
compelling reasons to consider the use of these frequencies.
From the perspective of ESM sensor designs, it is clear that
together with the need for higher sensitivity, greater
frequency coverage and higher DF accuracy, it is also
important that propagation effects be taken into account in
the structure of the receiving antenna. This paper has
addressed some of the consequences for signal propagation
in the maritime environment that can influence the design
of effective antenna apertures.
10. REFERENCES
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G.S. Woods, “Implications of the evaporation duct for
microwave radio path design over tropical oceans in
Northern Australia”, Workshop on Applications of
Radio Science, WARS02, National Committee for Radio
Science, (2002)
[2] A.S. Kulessa, M.L. Heron, and G.S. Woods,
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marine
environment”,
Proceedings
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URSI
Commission F on Climatic Parameters in Radiowave
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