The use of small drones is very popular over the past few years, mostly because of the different needs of users all around the world. However, there is little knowledge of the actual legislation and irresponsibility of the hobbyists using drones.
A lot of them actually use drones irresponsibly and increase the risk of serious accidents with a lot of aircrafts involved – especially around airports. This is the main aim of one paper submitted and published by Vaclav Vlasak, Vladimir Schejbal and Dusan Cermak, all part of the Faculty of Transport Engineering in Pardubice, Czech Republic.
The Number of Drones Around Airports is Increasing
As the stats in the article taken from German’s air control say, 15 drones broke the airspace of airports in 2015, followed by 64 in 2016 and 88 drones in 2017 respectively. As the authors conclude in the introduction, it seems like the tendency is increasing every year.
On the other hand, drones are very small in the Radar Cross Section (RCS) and as such cannot be detected by conventional radars. The radars must have high sensitivity to spot the drones. However, if they are so sensitive, it would also detect more ground clutter, jammers and many no-interest targets, which is what actually creates a complicated environment around airports.
The authors of this article further explain a system that will help aircraft officials spot drones – sectioned under ‘Targets Analysis.’ As they wrote:
“An important prerequisite for distinguishing targets is a thorough analysis of the specific features of specific types of targets. A large amount of information can be obtained from spectral properties using Doppler MTD processing. With this technique, it is possible, as with the simpler MTI method, to distinguish the reflection of a moving object from ground clutter. Additionally, it is possible to get an overview of the speeds that the object moves. ”
Defining the Discriminators Through a System Based on Machine Learning
Aside from this proposed system, they also list a number of ways in which the radar can define the so-called discriminators which tend to add up (irrelevant) information when spotting drones. Through proper use of parameters and solid classification, the system is based on machine learning and spotting actual drones in an airport environment.
All in all, the article presents a solid way to recognize different targets detected by the primary radar and therefore distinguish the target from other objects in the monitored area – preferably around airports and adjacent surroundings.
From this, the basic idea is to get information from a radar signal about a target using the new approach in signal processing. Through the use of their system and machine learning, the authors aim to solve the class problem and improve the features of existing radar systems.
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