Applications of
Artificial Neural Networks
to Ionograms
by
Markus Hagenbuchner
markus@neuro.informatik.uni-ulm.de
Supervised by
John A. Fulcher
john@cs.uow.edu.au
University of Wollongong
June 26, 1996
Department of Computer Science
Overview
The aim of this project is to determine methods to extract main F-layer
traces from either parametric or graphical inputs using Artificial Neural
Network techniques.
Various networks, learning rules and preprocessing methods are compared
by their performance and ability of being realistic applications.
The project is split into two parts:
- The usage of F-layer specific parameters (e.g. layer thickness and height) to train networks. This is discussed in section 3.
- Using the photographic record (ionogram) of an ionosonde as network input, which is presented in section 4.
An overview to simulators, network architectures and learning algorithms used in this project is given in section2.
Motivation
In 1901, Marconi established the first radiowave transmision between
Europe and North America. Later, in 1924, Kennely and Heaviside independendly
suggested that this communication was only possible because of the reflection
of radio signals by a conducting layer near 80km altitude. Radiowave methods
later led to the first quantitative studies of this layer, through analysis
of emitted signals reflected to the surface.
This layer plays an important role in the transmission of certain radio
waves, which can be reflected or refracted if the frequency is below 30MHz
(used for shortwave radio transmissions), or transmitted if their frequency
is above this limit (used for television transmissions and satelite communications).
Introduction
As first guessed by Heaviside and Kennely, the layers in the atmosphere
that reflect radio waves indeed contain charged particles, namely ions
and free electrons. In later experiments it was shown that radio waves
travel faster within an ionized atmosphere than in air without ions. This
change of speed is greater the smaller the frequency. It was also found
that for higher frequencies more charged particles are needed to reflect
the wave. What this means is that a high frequency wave will travel to
higher altitudes in the atmosphere than radio waves with a lower frequency.
With this knowledge and the theory that the concentration of ions gradually
changes within an ionized layer, the reflection of radio waves can be explained.
Figure 1 shows that wave front A travels more rapidly than B, since the speed of a
wave is faster the higher the concentration of ions.
---(Graphic missing)---
Figure 1: Reflection of radio waves in the ionosphere.
A definition was made by general agreement so that the part of the atmosphere
that contains sufficient ions to affect the travel of radio waves was called
ionosphere.
The ionosphere starts at an altitude of about 60km and is subdivided into
4 layers according to the occurance of peaks in the height distribution
of electrons. Those peaks occur near heights of 70, 100, 170 and 200 km
and are said to belong to the D-layer, E-layer, F1-layer and the F2-layer
respectively.
The ionosphere changes its state throughout the solar cycle, as well as throughout
the day and according to the seasons. The current state is investigated by a simple
apparatus named {\bf ionosonde} that sends radio signals towards the sky
and receives the reflected signal. It records the frequency of the wave
and the time delay between sending and receiving. This delay can be used
to calculate the apparent height of the reflectin ionized layer. The ionosonde
produces a photographic record called an ionogram (Figure 2), in which the time delay
is plotted against the frequency.
---(Graphic missing)---
Figure 2: Noisy, real world ionogram.
Vertical lines are caused by fixed-frequency radio transmitters. The horizontal line on
the bottom represents the E-layer reflection. The nosy curve above are the F-layer
reflections. The two weaker signals above show 2-hop and 3-hop F-layer reflections,
where a reflected signal bounces back from the Earth's surface once or twice before it
reaches the receiver.
For this project we look mainly at the F1 and F2-layer reflections, as
these reflect shortwave (HF) signals, which is most useful for radio transmissions.
As the F1 and F2 layers are not sharply separated and the F1-layer frequently
(and always at night) disappears, the layers above the E-layer are often
treated as a single F-layer.
There are two possible paths that radio waves can take when they are reflected
in a thick ionized F layer (Figure 3).
---(Graphic missing)---
Figure 3: Two possible signal paths.
The high angle path is the longer path and has therefore a greater delay
than the shorter low angle path. The result of this is that in transmitting
radio waves the receiver obtains signals with an echo.
---(Graphic missing)---
Figure 4: The main trace of an ionogram (1-hop F-layer reflection).
The high- and low angle paths come close together as the frequency increases
and join at the nose (also called the junction frequency), as shown in Figure
4. Beyond this point the reflection rapidly cuts off as there are insufficient charged
particles to reflect these high frequency waves. The most powerful reflection
occurs exactly at the nose frequency. It is therefore very important for
radio transmissions to know this nose frequency, so that a receiver gets
a clean and clear signal with maximum signal strength.
Unfortunately the ionosphere is already heavily used for radio transmissions.
Also magnetic storms (caused by high sunspot activities) in the upper atmosphere
often cause noise which distorts the shape of an ionogram. In addition,
the high angle path of a radio wave is often very weak, so that the characteristic
ionogram does not have a nosey shape. This makes it very difficult and
sometimes impossible to identify the nose frequency.
Simulators
Radio waves obey known physical laws. So a simulation of natural ionograms
on a computer is possible and would provide a time and money saving tool.
Jinoi, a simulater of this type was programmed by DSTO (Defense,
Science and Technology Organization). This simulator produces the main
trace of F-layer reflections. Unfortunately it was found that Jinoi has
some minor bugs so that it sometimes produces ionograms that are obviously
impossible in the real world, and even produces NaN errors or locks up.
Undoubtedly the physical laws behind this approach are highly complex so
that it is impossible for todays computers to handle all the cases that
could happen in real life. In fact the production of 100% accurate ionograms
would require a simulator that handles the behaviour of the huge number
of ions that exist in the atmosphere, as well as airplanes, winds and sunspot
activity, that could affect radio waves. From that point of view Jinoi
works well although a postprocessing of its output is necessary to filter
out 'bad' ionograms (Ionograms that had a negative frequency, more than
one nose, a nose that shows to the left or had a high- and low angle path
but no nose are found to be impossible in the real world and defined to
be 'bad'.).
Early in the life of this project an upgrade version of Jinoi, called Jornoi,
was provided by DSTO. Jornoi is much more stable than Jinoi, and produces
mostly reasonable ionogramms. The output, however, still contains about
8% of ionograms which where found to be 'bad'.
One aim of this project is to produce a simulator that produces Jinoi (or
Jornoi) like output by using Artificial Neural Network (ANN) techniques.
A second aim is the extraction of the main traces out of real ionograms,
again by using ANNs.
The University of Wollongong holds the copyright to this document. This is
the reason why the main part had to be cut out here. If you are
interested to get a copy of the full version of this document please
contact John Fulcher john.cs.uow.edu.au
at the University of Wollongong.
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markus@neuro.informatik.uni-ulm.de --
last update: Jan 17, 1998