Detection, Tracking, and Interdiction for Amateur Drones

Güvenç, İsmail; Koohifar, Farshad; Singh, Simran; Sichitiu, Mihail L.; Matolak, David W.

doi:10.1109/mcom.2018.1700455

Cited by 246 publications

(152 citation statements)

References 12 publications

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“…In the prediction mode, we monitor the ongoing behavior of a swarm and predict the future locations of all involved agents. Such functionality is helpful, for example, in interdiction tasks [13], [14], [15] in which a system has to generate recommendations on the least-cost and most-effective actions needed to thwart or disrupt the threat posed by an adversarial swarm. SwarmNet can also be used as a policy to generate a completely new swarm, which we refer to as clone swarms.…”

Section: Methodsmentioning

confidence: 99%

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Zhou

Sefair

Walker

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose SwarmNet -a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Tested on artificially generated swarm motion data, the network achieves high levels of prediction accuracy and imitation authenticity. We compare our model to previous approaches for modelling interaction systems and show how modifying components of other models gradually approaches the performance of ours. Finally, we also discuss an extension of SwarmNet that can deal with nondeterministic, noisy, and uncertain environments, as often found in robotics applications.

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Section: Methodsmentioning

confidence: 99%

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Zhou

Sefair

Walker

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The authors of [ 6 ] developed a sensor able to detect high-resolution points within 0.5° × 0.5° by scanning the high-resolution laser pulses at high speed in AZ and EL directions, where intersection angle of the vertical direction (EL direction) is 0.003°, and the intersection angle of horizontal (AZ direction) is 0.011°. Potentially harmful cyber and physical threats that may arise from unauthorized flying of UAVs over forbidden zones is analyzed by other researchers [ 7 ], along with reviewing various UAV detection techniques based on ambient radio frequency signals (emitted from drones), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs.…”

Section: Related Workmentioning

confidence: 99%

Development of an Acoustic System for UAV Detection

Dumitrescu

Minea

Costea

et al. 2020

Sensors

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The purpose of this paper is to investigate the possibility of developing and using an intelligent, flexible, and reliable acoustic system, designed to discover, locate, and transmit the position of unmanned aerial vehicles (UAVs). Such an application is very useful for monitoring sensitive areas and land territories subject to privacy. The software functional components of the proposed detection and location algorithm were developed employing acoustic signal analysis and concurrent neural networks (CoNNs). An analysis of the detection and tracking performance for remotely piloted aircraft systems (RPASs), measured with a dedicated spiral microphone array with MEMS microphones, was also performed. The detection and tracking algorithms were implemented based on spectrograms decomposition and adaptive filters. In this research, spectrograms with Cohen class decomposition, log-Mel spectrograms, harmonic-percussive source separation and raw audio waveforms of the audio sample, collected from the spiral microphone array—as an input to the Concurrent Neural Networks were used, in order to determine and classify the number of detected drones in the perimeter of interest.

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“…Organized crime groups have taken advantage of advances in consumer drone technology and use this technology to circumvent law enforcement efforts. Smart cities ensure security in an advanced environment by using drone technology to interdict enemy drones [5]. However, current UAV technology relies on using various signals for communication and maneuvering in the field.…”

Section: Adversarial Swarmsmentioning

confidence: 99%

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Cooley

Wolf

Borowczak

2018

2018 IEEE International Smart Cities Conference (ISC2)

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Unmanned Aerial Vehicles (UAVs), referenced as drones, have advanced to consumer adoption for hobby and business use. Drone applications, such as infrastructure technology, security mechanisms, and resource delivery, are just the starting point. More complex tasks are possible through the use of UAV swarms. These tasks increase the potential impacts that drones will have on smart cities, modern cities which have fully adopted technology in order to enhance daily operations as well as the welfare of it's citizens. Smart cities not only consist of static mesh networks of sensors, but can contain dynamic aspects as well including both ground and air based autonomous vehicles.Networked computational devices require paramount security to ensure the safety of a city. To accomplish such high levels of security, services rely on secure-by-design protocols, impervious to security threats. Given the large number of sensors, autonomous vehicles, and other advancements, smart cities necessitates this level of security. The SHARK protocol (Secure, Heterogeneous, Autonomous, and Rotational Knowledge for Swarms) ensures this kind of security by allowing for new applications for UAV swarm technology. Enabling drones to circle a target without a centralized control or selecting lead agents, the SHARKS protocol performs organized movement among agents without creating a central point for attackers to target. Through comparisons on the stability of the protocol in different settings, experiments demonstrate the efficiency and capacity of the SHARKS protocol.

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Detection, Tracking, and Interdiction for Amateur Drones

Cited by 246 publications

References 12 publications

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Development of an Acoustic System for UAV Detection

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

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