Abstract
Safety, security, and privacy are three critical concerns affiliated with the use of drones in everyday life. Considering their ever-shrinking sizes and capabilities, being aware of drone activities in the vicinity becomes an important surveillance item. Therefore, keeping track of drones and preferably their controllers should be included into the already-existing security measures. In this study, a frequency hopping spread spectrum (FHSS) type drone controller signal detection and emitter direction finding framework is proposed to achieve aforementioned goals. Since drone communications signals generally coexist with other FHSS signals in 2.4 GHz industrial, scientific, and medical (ISM) band, first, a method based on cyclostationarity analysis is proposed to distinguish the drone radio controller signals from other signals utilizing 2.4 GHz ISM band. Then, a variant of short-term Fourier transform is introduced to extract the parameters of detected drone remote controller 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.
Highlights
Drones pervade the modern civilian life almost in every aspect
MEASUREMENT RESULTS In this study, frequency hopping (FH) signals have been reconstructed with respect to parameters of each hop, and the performance results of two different angle of arrival (AoA) algorithms for FH signals are evaluated under LOS and NLOS cases
The drone radio controller (RC) signal detection is accomplished by applying cyclostationarity feature detection (CFD) to provide distinction from other signals
Summary
Drones pervade the modern civilian life almost in every aspect. Surveying and mapping lands, logistics, surveillance, The associate editor coordinating the review of this manuscript and approving it for publication was Chengpeng Hao. Even though drones exploit FHSS techniques, one should note that they operate in civilian airspaces by occupying industrial, scientific, and medical (ISM) bands shared by many other telecommunication links which employ FHSS as well In this respect, there are various algorithms developed for signal detection over the years such as autocorrelation–based, wavelet-based, and time–frequency analysis based algorithms. It is important to state here that the SBS method benefits from the following two key points: firstly, it operates with a mechanically agile directional single antenna, and secondly, it does not require any baseband signal processing. These key points come at the expense of several shortcomings. 3) The performance of the proposed method is empirically assessed and it is shown that the resultant framework can find the direction of arrival of the drone RC signal with an average of 1.39 degrees of error
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