HorizonBlock: Implementation of an Autonomous Counter-Drone System

Souli, Nicolas; Makrigiorgis, Rafael; Zacharia, Angelos; Petrides, Panayiotis; Lazanas, A.; Valianti, Panayiota; Kolios, Panayiotis; Ellinas, Georgios

doi:10.1109/icuas48674.2020.9213871

Cited by 20 publications

(14 citation statements)

References 22 publications

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“…Such methods were presented in [5], [6], [7] and [8] by the author and his team offering enhancement of the friendliness of user humansystem interfaces. Recent systems such as those described in [9], [10], [11], [12], [13] and [14] lack such functionalities and may benefit from the leveraging of the techniques described in the present paper making the task of the supervising user much safer and easier.…”

Section: Discussionmentioning

confidence: 99%

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Kontos¹

2021

IJAIA

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Research on explanation is currently of intense interest as documented in the DARPA 2021 investments reported by the USA Department of Defense. An emerging theme for explanation techniques research is their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV defense systems. In the present paper a novel proposal based on natural language processing technology concerning explanatory discourse using relations is briefly described. The proposal is based on the use of relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the supervision that a user must exercise on an autonomous defense system in order to minimize the risk of wrong mitigation actions and unnecessary spending of ammunition.

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Section: Discussionmentioning

confidence: 99%

Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Kontos¹

2021

IJAIA

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“…The tracker first estimates the distance between the target and receivers by (8). Subsequently, the tracker estimates the location of the target by calculating the solution of (23)- (26) as…”

Section: Trilateration Based Tracking Algorithmmentioning

confidence: 99%

“…Somebody can also use unauthorized drones in order to transport illegal objects and invade privacy, such as unlawful filming. Recently, with Saudi Arabia's oil facilities under the threat of terrorism by drones, the need for investment and research into anti-drone technologies has emerged worldwide [4][5][6][7][8][9][10][11][12]. Anti-drone stands for a system that detects, identifies, tracks, and incapacitates illegal drones.…”

Section: Introductionmentioning

confidence: 99%

“…However, radar is expensive to install and maintain, and it is hard to detect small-size drones. As a radar replacement technology to solve this problem, various methods were considered, such as using the physical characteristics of a RF signal that is used for drone control distinguished from a mobile device [5], and monitoring drones by camera images [6][7][8], and detecting an acoustic signal that is generated by a drone [9]. However, the method using the physical characteristics of the RF signal can be hard to detect illegal drones if drones use RF signals that resemble the RF characteristics of other mobile devices.…”

Section: Introductionmentioning

confidence: 99%

“…However, we propose an aerial solution while using a tracking drone, not the stationary solution above. Aerial solutions approach targets, so you can expect higher accuracy and reduce the threat to legitimate communication systems [8].…”

Section: Introductionmentioning

confidence: 99%

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Position Tracking Techniques Using Multiple Receivers for Anti-Drone Systems

Shin

Kim

Ban

et al. 2020

Sensors

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The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target’s location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system’s performance in terms of the average number of movements that are required to track and the tracking success rate.

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