Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO

A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To...

Classification of Birds and Drones Exploiting Kinematic Properties for Surveillance Radars Using Machine Learning Techniques

Small UAVs and in particular the class of micro-UAVs, whose mass is below 2 kg, are constantly rising in popularity for personal as well as professional use, since they are beneficial in many fields such as defense, transportation, monitoring and agriculture. In spite of their advantages, UAVs can be used for terrorist attacks to fly over...

Automatic Classification of Unmanned Aerial Vehicles with Radars On-The-Move

Drone detection and tracking systems are nowadays a requirement in most public, private and political events, because of the increasing risk of unintentional or malicious misuse of these platforms. Moreover, in order to ensure adequate protection, full spatial coverage is a must for every such system. However, the research literature focuses on...

Algorithms for Separation of Secondary Surveillance Radar Replies

Air Traffic Control (ATC) centers aim at ensuring safety of aircrafts cruising in their area. The information required to face this mission includes the data provided by primary and Secondary Surveillance Radar (SSR). The first one indicates the presence of an aircraft, whereas the second gives information on its identity and altitude. All...

A method and measures to evaluate trackers for air traffic control

The general setup of the evaluation of a radar tracking algorithm in a civil ATC-system has been considered from an operational point of view. Both the present and the potential future use of trackdate has been taken in account. The result is a method to evaluate trackers for ATC on the basis of live-traffic data, under the assumption that a...

Comparison of a jump-diffusion tracker with a Kalman tracker: an evaluation with emphasis on air traffic control

A sophisticated starting point for a probabilistic approach to the radar tracking problem is a Markov jump-diffusion model for the aircraft dynamics, its control and the radar measurements. From nonlinear filtering theory, a closed form description of the evolution of the conditional distribution of this Markov process can be obtained. This jump...