Signal detection and parameter estimation of frequency hopping signals

Abstract

Unmanned aerial vehicles (UAVs) have become a prevalent part of the daily life with their applications to many fields such as mapping and surveying, transportation, surveillance, law enforcement, aerial imaging and agriculture. Besides the aforementioned use of UAVs in many areas, one should keep in mind that UAVs can also be used dangerously to create unwanted incidents especially when they are diverted to the sensitive airspace near airports and their presence may cause accidents which can result in fatal crashes. Moreover, UAVs can be utilized for collecting information about people, organizations, and companies without their consent. Therefore, identification and direction of arrival estimation of UAV systems/remote controllers and their communication are great importance, especially to prevent unwanted situations. In this context, it is known that most of the communication between the UAVs and wireless radio controller (RC) utilize the spread spectrum technology of frequency hopping spread spectrum (FHSS) on ISM band at 2.4 GHz. Therefore a method to detect, classify and estimate the direction of arrival of these kinds of signals in this band would lead to the identification of the communication between the UAV and the controller. Thus, this thesis attempts to address solutions for identifying and direction of arrival estimation of FHSS signals. In Chapter I, open issues and the state-of-the-art solutions to the open issues in FHSS signal detection, identification and direction of arrival estimation are given. Moreover, in Chapter II, mathematical preliminaries of FHSS signal characteristics are provided. In Chapter III, signal detection and parameter estimation are discussed by focusing on cyclostationarity signal and time-frequency analyses. First, a method based on cyclostationarity analysis is applied to distinguish the FHSS signals. Furthermore, short-time Fourier transform (STFT) based blind signal detection and clustering are employed to reconstruct the correct hops of the FHSS signal. Therefore, if there is an interference signal, outliers are removed from the parameters table according to the spectral localization of the signals. Furthermore, the literature utilizes the simulated data instead of over-the-air signals in general and these simulations assume that there is no time guards between hops. This assumption makes differentiation of frequency-hopping (FH) signals easier, however, many hopping signals use time guards and also these time guards are different for different signal sources. In Chapter IV, direction of arrival estimation for FHSS signals are studied by utilizing MUSIC algorithm which is a high-resolution subspace-based direction-finding algorithm. A variant of STFT is introduced to extract the parameters of detected FHSS signals. The correct hopping signals are then aggregated based on the clustered parameters to obtain their combined baseband equivalent signal. Furthermore, the resampling process is applied to reduce the unrelated samples in the spectrum and represent the spectrum with the reconstructed signal, which has a much lower bandwidth than the spread bandwidth. Finally, two different multiple signal classification algorithms are utilized to estimate the direction of the drone controller relative to the receiving system. In order to validate the overall performance of the proposed method, the introduced framework is implemented on hardware platforms and tested under real-world conditions. A uniform linear antenna array is utilized to capture over-the-air signals in hilly terrain suburban environments by considering both line-of-sight and non-line-of-sight cases. Direction estimation performance is presented in a comparative manner and relevant discussions are provided.