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• W656479245 abstract "Since early experimentation in the late 1800's, wireless communication has become increasinglyimportant and has been widely adopted by civilian and military markets worldwide.The proliferation of wireless communication systems presents new challenges, threatsand opportunities for society and government institutions. Although the possibility of infringingprivacy laws exist, electronic surveillance has become an important capabilityin military, counter-terrorism and law-enforcement operations. Through interception ofwireless communication signals, an advantage may be gained by extracting intelligencefrom, or interfering with, communication signals of an adversary. Interception can onlybe performed once the presence of the communication signal is detected. However, communicationsignals are typically not intended for reception by third parties and securitymechanisms 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 useand manage system resources, such as the available radio frequency (RF) spectrum, havebeen developed and are implemented in these devices to ensure co-existence and to limitinterference. Communication systems are also designed to minimise transmission powerdynamically, which brings about several advantages, such as enhanced battery life formobile users and lower detection probability in military applications. Techniques to shareresources among several users are also employed in order to increase system capacity andavailability. Detecting the presence of a certain communication signal within the resultantdense signal environment is therefore challenging, especially if the intercept receiver doesnot have accurate knowledge of the parameters being used by the target communicationsystem. The signal of interest will typically be weak, hidden in background noise andamong several other competing communication signals.The detection of communication signals, and specifically weak signals, forms an integralpart 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 radiosystem must be obtained before further action can be taken to counter potentially hostilecommunication transmissions. Efficient detection of weak communication signals willtherefore enhance the detection capability of communication intercept receivers, and willprovide an improved capability to perform interception, direction finding and jamming ofthese hidden transmissions.This thesis considers the non-cooperative or blind detection of a specific class of covertcommunication signals, known as direct sequence spread spectrum (DSSS). DSSS is a lowprobability of detection (LPD) communications technique, initially developed for militaryapplication to hide transmitted messages below the noise floor in order to avoid detectionby potential enemy interceptors. DSSS has also become popular in non-military communicationsystems 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 communicationin mobile radio channels. As DSSS was designed as a covert communication technique,detecting and demodulating DSSS transmissions present a significant challenge, especiallyin the non-cooperative context.The performance of detection algorithms can be expressed in terms of the probability of detectionover a range of signal-to-noise ratios (SNRs), although computational complexityshould also be taken into account. Sophisticated algorithms which provide high detectionprobabilities usually also have high computational demands, which will limit their implementationin real-time detection systems. Existing detection techniques are investigatedand evaluated in this thesis through mathematical analysis and Monte-Carlo computersimulation, in terms of both detection probability and computational complexity. Mostexisting detection techniques rely on differentiating between the statistical properties ofthe signal and the noise in which the signal is potentially hidden, using test statisticsbased on either energy or correlation characteristics. New and improved detection andestimation techniques, based on similar concepts and eigen analysis, are presented andevaluated in this thesis.The main body of this thesis consists of three published journal articles, which resultedfrom the Ph.D. research work, embedded into the text. The first publication presents anapproximation to a statistical distribution which can be used to predict the performanceof the eigen detection techniques presented here. The second publication presents twonew semi-blind DSSS detection techniques, and the third publication considers the blindestimation of the sequence length of DSSS spreading codes. Sequence length estimationis important as several semi-blind DSSS detection and estimation techniques require thesequence length as input parameter." @default.
• W656479245 created "2016-06-24" @default.
• W656479245 creator A5075364038 @default.
• W656479245 date "2014-12-01" @default.
• W656479245 modified "2023-09-26" @default.
• W656479245 title "Detection of direct sequence spread spectrum signals" @default.
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