Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need to consider kinetic features and signal signatures, such as micro-Doppler, in ATR techniques to efficiently recognize small drones. The authors also present a comprehensive drone detection radar system design that balances detection and tracking requirements, incorporating parameter adjustment based on scattering region theory. They offer an example of a performance improvement achieved using feedback and situational awareness mechanisms with the integrated ATR capabilities. Furthermore, the paper examines challenges related to one-way attack drones and explores the potential of cognitive radar as a solution. The integration of ATR capabilities transforms a 3D radar system into a 4D radar system, resulting in improved drone detection performance. These advancements are useful in military, civilian, and commercial applications, and ongoing research and development efforts are essential to keep radar systems effective and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie

UAS Concept of Operations and Vehicle Technologies Demonstration

In 2017 and 2018, under National Aeronautics and Space Administration (NASA) sponsorship, the New York Unmanned Aircraft Systems (UAS) Test Site and Northeast UAS Airspace Integration Research (NUAIR) Alliance conducted a year-long research project that culminated in a UAS technology flight demonstration. The research project included the creation of a concept of operations, and development and demonstration of UAS technologies. The concept of operations was focused on an unmanned aircraft transiting from cruise through Class E airspace into a high-density urban terminal environment. The terminal environment in which the test was conducted was Griffiss International Airport, under Syracuse Air Traffic Control (ATC) approach control and Griffiss control tower. Employing an Aurora Centaur optionally piloted aircraft (OPA), this project explored six scenarios aimed at advancing UAS integration into the National Airspace System (NAS) under both nominal and off-nominal conditions. Off-nominal conditions were defined to include complete loss of the communications link between the remote pilots control station on the ground and the aircraft. The off-nominal scenarios that were investigated included lost-link conditions with and without link recovery, an automated ATC initiated go-around, autonomous rerouting around a dynamic airspace obstruction (in this case simulated weather), and autonomous taxi operations to clear the runway

A Review of Automatic Classification of Drones Using Radar:Key Considerations, Performance Evaluation and Prospects

Automatic target classification or recognition is a critical capability in non-cooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognising targets, including miniature unmanned air systems or drones (i.e., small, mini, micro and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar

Development of a detect-and-avoid sensor solution for the integration of a group 3 large unmanned aircraft system into the national airspace system

Unmanned Aircraft Systems (UAS) face one common challenge when integrating with the existing manned aircraft population in the National Airspace System (NAS). To unlock the full efficiency of UAS, the UAS integrator must comply with an onboard pilot’s requirement to see-and-avoid other aircraft while operating. Commercially available Detect-and-Avoid (DAA) sensor technologies have been developed to attempt to comply with this requirement. UAS integrators must use these sensors to meet or exceed the performance of a human pilot. This thesis covers research done to integrate an array of commercially made DAA sensors with a large Group 3 UAS both in hardware and software that was later flight tested and evaluated for usability. A fast-time simulation is presented using the principles of the National Aeronautics and Space Administration\u27s (NASA) Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS). Last, open-source tools are presented to assist future integrators in validating their DAA solutions

Revisión de algoritmos, métodos y técnicas para la detección de UAVs y UAS en aplicaciones de audio, radiofrecuencia y video

Unmanned Aerial Vehicles (UAVs), also known as drones, have had an exponential evolution in recent times due in large part to the development of technologies that enhance the development of these devices. This has resulted in increasingly affordable and better-equipped artifacts, which implies their application in new fields such as agriculture, transport, monitoring, and aerial photography. However, drones have also been used in terrorist acts, privacy violations, and espionage, in addition to involuntary accidents in high-risk zones such as airports. In response to these events, multiple technologies have been introduced to control and monitor the airspace in order to ensure protection in risk areas. This paper is a review of the state of the art of the techniques, methods, and algorithms used in video, radiofrequency, and audio-based applications to detect UAVs and Unmanned Aircraft Systems (UAS). This study can serve as a starting point to develop future drone detection systems with the most convenient technologies that meet certain requirements of optimal scalability, portability, reliability, and availability.Los vehículos aéreos no tripulados, conocidos también como drones, han tenido una evolución exponencial en los últimos tiempos, debido en gran parte al desarrollo de las tecnologías que potencian su desarrollo, lo cual ha desencadenado en artefactos cada vez más asequibles y con mejores prestaciones, lo que implica el desarrollo de nuevas aplicaciones como agricultura, transporte, monitoreo, fotografía aérea, entre otras. No obstante, los drones se han utilizado también en actos terroristas, violaciones a la privacidad y espionaje, además de haber producido accidentes involuntarios en zonas de alto riesgo de operación como aeropuertos. En respuesta a dichos eventos, aparecen tecnologías que permiten controlar y monitorear el espacio aéreo, con el fin de garantizar la protección en zonas de riesgo. En este artículo se realiza un estudio del estado del arte de la técnicas, métodos y algoritmos basados en video, en análisis de sonido y en radio frecuencia, para tener un punto de partida que permita el desarrollo en el futuro de un sistema de detección de drones, con las tecnologías más propicias, según los requerimientos que puedan ser planteados con las características de escalabilidad, portabilidad, confiabilidad y disponibilidad óptimas