Multispectral Detection of Commercial Unmanned Aerial Vehicles

Abstract

The fight against unmanned vehicles is nothing new; however, especially with the arrival of new technologies that are easily accessible for the wider population, new problems are arising. The deployment of small unmanned aerial vehicles (UAVs) by paramilitary organizations during conflicts around the world has become a reality, non-lethal “paparazzi” actions have become a common practice, and it is only a matter of time until the population faces lethal attacks. The basic prerequisite for direct defense against attacking UAVs is their detection. The authors of this paper analysed the possibility of detecting flying aircraft in several different electro-magnetic spectrum bands. Firstly, methods based on calculations and simulations were chosen, and experiments in laboratories and measurements of the exterior were subsequently performed. As a result, values of the radar cross section (RCS), the noise level, the surface temperature, and optical as well as acoustic traces of tested devices were quantified. The outputs obtained from calculated, simulated, and experimentally detected values were found via UAV detection distances using specific sensors working in corresponding parts of the frequency spectrum.

Influence of Rotor Dynamic Scattering on Helicopter Radar Cross-Section

“…Historically, research on the radar stealth of helicopters and rotor components is mostly based on quasi-static methods, and their calculation results are also static RCS or a small number of quasi-static results [23,24]. The core of QSP is to discretize many sample states.…”

A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System

“…For example, the report by Bard College's Center for the Study of the Drone [3] found that 327 incidents between December 2013 and September 2015 (USA only) posed a "proximity danger" where an unmanned aircraft got within 500 feet (152.4 m) of a plane, helicopter, or other manned aircraft (involved multiengine jet aircraft). The above mentioned, as well as similar accidents, have caused many institutions to Many scientists and engineers involved in the development of anti-drone systems agree that a multi-sensor system could provide a higher probability of drone detection than a single sensor [6][7][8][9][10][11][12]. Experiments also suggest that one of the most efficient detectors of UAV is a frequency modulated continuous wave (FMCW) radar [6][7][8][9][10][11][12].…”

“…The above mentioned, as well as similar accidents, have caused many institutions to Many scientists and engineers involved in the development of anti-drone systems agree that a multi-sensor system could provide a higher probability of drone detection than a single sensor [6][7][8][9][10][11][12]. Experiments also suggest that one of the most efficient detectors of UAV is a frequency modulated continuous wave (FMCW) radar [6][7][8][9][10][11][12]. It has a big detection range, transmits low power [13], and is independent of electromagnetic signals and sounds emitted by drones (UAV operating in a fully autonomous mode and gliding can be detected, too).…”

“…Long-range FMCW radars used in non-military anti-drone systems have detection ranges of micro-drones (DJI Phantom sizes) not exceeding 3 km, due to the small radar cross section (RCS) of such objects [14][15][16][17]. However, the classification range has a practical meaning, as anti-drone radars also detect birds and other small objects.…”

Signal processing for direction finding and range determining to small UAVs in the optical and infrared ranges

“…Для обнаружения и пеленгации БПЛА используют радиолокационные, радиочастотные, акустические и оптико-электронные методы, а также их комплексирование [1]. Среди оптико-электронных методов определения координат объектов выделяют лазерное сканирование, структурированное освещение и пассивные стерео-бинокулярные системы [2].…”