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Abstract

Since early experimentation in the late 1800's, wireless communication has become increasingly
important and has been widely adopted by civilian and military markets worldwide.
The proliferation of wireless communication systems presents new challenges, threats
and opportunities for society and government institutions. Although the possibility of infringing
privacy laws exist, electronic surveillance has become an important capability
in military, counter-terrorism and law-enforcement operations. Through interception of
wireless communication signals, an advantage may be gained by extracting intelligence
from, or interfering with, communication signals of an adversary. Interception can only
be performed once the presence of the communication signal is detected. However, communication
signals are typically not intended for reception by third parties and security
mechanisms are often employed to protect communication transmissions from compromise.
Sophisticated techniques are therefore required to reliably detect the presence of,
and to extract information from, the communication signal of interest.
Due to the ubiquitous use of wireless communication devices, techniques to efficiently use
and manage system resources, such as the available radio frequency (RF) spectrum, have
been developed and are implemented in these devices to ensure co-existence and to limit
interference. Communication systems are also designed to minimise transmission power
dynamically, which brings about several advantages, such as enhanced battery life for
mobile users and lower detection probability in military applications. Techniques to share
resources among several users are also employed in order to increase system capacity and
availability. Detecting the presence of a certain communication signal within the resultant
dense signal environment is therefore challenging, especially if the intercept receiver does
not have accurate knowledge of the parameters being used by the target communication
system. The signal of interest will typically be weak, hidden in background noise and
among several other competing communication signals.
The detection of communication signals, and specifically weak signals, forms an integral
part of modern electronic warfare (EW) in applications of communication surveillance.
Signal detection is foundational in extracting parameter values and communications intelligence
(COMINT) from radio transmissions, which are important components of communications EW. Knowledge of the communication parameter values of the target radio
system must be obtained before further action can be taken to counter potentially hostile
communication transmissions. Efficient detection of weak communication signals will
therefore enhance the detection capability of communication intercept receivers, and will
provide an improved capability to perform interception, direction finding and jamming of
these hidden transmissions.
This thesis considers the non-cooperative or blind detection of a specific class of covert
communication signals, known as direct sequence spread spectrum (DSSS). DSSS is a low
probability of detection (LPD) communications technique, initially developed for military
application to hide transmitted messages below the noise
floor in order to avoid detection
by potential enemy interceptors. DSSS has also become popular in non-military communication
systems and is widely implemented in existing wireless communication standards.
The popularity of DSSS is due to its interference-rejection, multipath-resistance,
co-existence and transmission-security properties, which are desirable for communication
in mobile radio channels. As DSSS was designed as a covert communication technique,
detecting and demodulating DSSS transmissions present a significant challenge, especially
in the non-cooperative context.
The performance of detection algorithms can be expressed in terms of the probability of detection
over a range of signal-to-noise ratios (SNRs), although computational complexity
should also be taken into account. Sophisticated algorithms which provide high detection
probabilities usually also have high computational demands, which will limit their implementation
in real-time detection systems. Existing detection techniques are investigated
and evaluated in this thesis through mathematical analysis and Monte-Carlo computer
simulation, in terms of both detection probability and computational complexity. Most
existing detection techniques rely on differentiating between the statistical properties of
the signal and the noise in which the signal is potentially hidden, using test statistics
based on either energy or correlation characteristics. New and improved detection and
estimation techniques, based on similar concepts and eigen analysis, are presented and
evaluated in this thesis.
The main body of this thesis consists of three published journal articles, which resulted
from the Ph.D. research work, embedded into the text. The first publication presents an
approximation to a statistical distribution which can be used to predict the performance
of the eigen detection techniques presented here. The second publication presents two
new semi-blind DSSS detection techniques, and the third publication considers the blind
estimation of the sequence length of DSSS spreading codes. Sequence length estimation
is important as several semi-blind DSSS detection and estimation techniques require the
sequence length as input parameter.

Item Type:	Thesis - PhD
Authors/Creators:	Vlok, JD
Keywords:	weak signal detection, blind signal detection and estimation, communication theory, electronic warfare, low probability of detection, Wishart matrix, eigen analysis, sequence estimation
Copyright Holders:	The Author
Copyright Information:	Copyright 2014 the Author
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