Detection of direct sequence spread spectrum signals

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.