Global Navigation Satellite Systems (GNSS) offer precise position estimation and navigation services outdoor but they are rarely accessible in strong multipath environments, such as indoor environments. Fortunately, several Signals of Opportunity (SoO), (such as RFID, Wi-Fi, Bluetooth, digital TV signals, etc.) are readily available in these environments, creating an opportunity for seamless positioning. Performance evolution of positioning can be achieved through contextual exploitation of SoO. The detection and identification of available SoO signals or of the signals which are most relevant to localization and the signal selection in an optimum way, according to designer defined optimality criteria, are important stages to enter such contextual awareness domain. Man-made modulated signals have certain properties which vary periodically in time and this time-varying periodical characteristics trigger what is known as cyclostationarity. Cyclostationarity analysis can be used, among others, as a tool for signal detection. Detected signals through cyclostationary features can be exploited as SoO. The main purpose of this thesis is to study and analyze the cyclostationarity properties of various SoO. An additional goal is to investigate whether such cyclostationarity properties can be used to detect, identify and distinguish the signals which are present in a certain frequency band.
The thesis is divided into two parts. In the literature review part, the physical layer study of several signals is given, by emphasizing the potential of SoO in positioning. In the implementation part, the possibility of signals detection through cyclostationary features is investigated through MATLAB simulations. Cyclostationary properties obtained through FFT accumulation Method (FAM) and statistical performance of detection are studied in the presence of stationary additive white Gaussian noise (AWGN). Besides that, the performance in signal detection using cyclostationary-based detector is also compared to the performance with the energy-based detectors, used as benchmarks.
The simulated result suggest that cyclostationary features can certainly detect the presence of signals in noise, but simple cases, such as one type of signal only and AWGN noise, are better addressed via traditional energy-based detection. However, cyclostationary features can exhibit advantages in other types of noises and in the presence of signal mixtures which in fact may fulfil one of the preliminary requirements of cognitive positioning
