Detecting and filtering multiple drone controller signals from background noise using bearing & amplitude data

In the light of significant recent scientific and technical developments, unmanned aerial vehicles (UAV) are now used in many areas and most developed countries constantly seek to develop their scientific capabilities whilst working on the use of these aircraft in many military and civilian fields. Despite the potential use of UAVs in many sectors, where new smaller UAVs have been developed in recent years, they pose significant concerns especially with regard to privacy and safety. Therefore, it is necessary to find way of detecting, localising and preventing these kinds of vehicles from penetrating sensitive areas such as airports, prisons and others. This paper presents firstly an analysis of measurements of Radio Frequency (RF) signals emitted from UAVs, collected as CSV file data, and secondly design a methodology to detect the drone angle from sensors using bearing data. We also report a signal pattern technique to detect and classify multiple drone controllers in the presence of background noise during the same period.