3D active and passive geolocation and tracking of Unmanned Aerial Systems

Defending an area of interest against small, inexpensive Unmanned Aerial Systems (UASs) poses a particuler set of challenges requiring novel methods of detecting, geolocating and tracking these potential threats in highly dynamic environments. These targets of interest tend to operate in two distinctive modes: RF active and passive. In RF active mode the target is typically communicating with a ground-station or some other system. In RF passive mode, the target has usually been pre-programmed to fly to specific waypoints, guided by on-board GPS, before or during its particular mission, which may include the capture of surveillance video and storing it locally and returning to the handler for data retrieval. Certainly, there are many other perturbations of these two modes but these capture the two ends of the geo-location space. In this paper we present a unified signal processing methodology for geo-locating and tracking which addresses both these extreme target types. The algorithm has been demonstrated to be used quite effectively in a suite of inexpensive distributed RF sensors. The general approach for RF active targets, includes using multiple sensors to receive the emitted RF signals directly from the target while for RF passive targets, a separate transmitter is used to illuminate the target, creating a multistatic radar capture. For both target types the distributed set of sensors jointly generate the Time Difference of Arrival (TDOA), Frequency Difference of Arrival (FDOA), and Frequency Rate Difference of Arrival (FRDOA) from directly received signals from RF Active targets or from reflected energy from RF Passive targets. From the TDOA, FDOA, and FRDOA, whether generated from a direct emitter or from a reflected target we demonstrate a distributed algorithm for geo-locating and tracking. This paper focusing on presenting the unified signal processing approach for accurately detecting, geolocating and tracking both active and passive targets as well as presenting simulation results demonstrating the performance of the algorithm for both target types.