Las Vegas, NV, December 24, 2019 --(PR.com
)-- Throughout recent years, non-military micro-unmanned aerial vehicles (micro-UAVs) or drones have conspicuously proliferated. In addition to recreational use by hobbyists, there is a growing interest in the use of micro-UAVs and drones for commercial applications. One of the main areas of use is precision farming, where micro-UAVs and drones make it easy to map and survey farmland for crop variability and phenology, weed and pest control crop dusting / spraying, irrigation management and livestock monitoring.
Other commercial applications of drones include critical infrastructure monitoring, package delivery, media and entertainment, as well as ad hoc access point Internet connectivity. Due to the potential advantages of micro-UAVs and drones, the Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA) have a joint plan to integrate commercial micro-UAVs into national airspace (NAS).
To date, several techniques have been introduced for micro-UAV and drone detection and classification. However, conventional radar-based techniques, which are widely deployed for detecting and identifying airplanes, mostly fail to detect commercial drones.
Alternative methods such as sound and visual-based detection are only suitable for short-range situations due to ambient noise. Some of these problems can be solved by radio frequency (RF) fingerprint-based drone detection technology. Nevertheless, the current trend in the RF fingerprint classification of micro-UAVs and drones focuses mainly on time-domain techniques that are not very effective. This is because time domain techniques are based on the assumption that there is an abrupt change at the starting point of the signal.
However, this assumption is not always valid when the transition between the transient and noise is more gradual. As a consequence, time domain techniques could possibly delay the detection of the transient signal. In the worst-case scenario, this may increase the probability of a missed target drone detection at a low signal-to-noise ratio (SNR).
RF-108 RF Based Drone Detection Radar is introduced by the need to address the aforementioned challenges. Due to the problems associated with the use of the time-domain transient analysis, a new approach for the drone detection and classification is developed by Dynamite Global Strategies, Inc. (DGS). In this approach, the time-domain signal is first transformed into the energy-time-frequency domain and the energy trajectory is computed.
In addition, the challenge of detecting micro-UAVs and drones (with extremely low RCS) is overcome as all that is required is to intercept the transmit signal from the drone controller. The detection range issue associated with visual and acoustic-based approaches can be overcome by using high-gain receiver antennas together with a highly sensitive RF spectrum receiver system to listen to drone controllers’ signals. The issue of ambient RF signal noise can be eliminated by using a variety of de-noise methods, for example, wavelet decomposition and band-pass filtering. Such advantages make the RF-108 RF Based Drone Detection System a promising solution.