Receding Horizon Multi-UAV Cooperative Tracking of Moving RF Source

Koohifar, Farshad; Kumbhar, Abhaykumar; Güvenç, İsmail

doi:10.1109/lcomm.2016.2603977

Cited by 74 publications

(50 citation statements)

References 10 publications

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“…Flight Time (s) RMS (m) POMDP [19] 724 12.4 Q-learning [21] 200  10 [20]: The aim of ML is to estimate the parameters of a statistical model by maximising a likelihood function. In this method, the joint likelihood function can be designed to estimate whether the target is detected by the drone or not.…”

Section: Methodsmentioning

confidence: 99%

Autonomous Flying WiFi Access Point

Nunns

Chen

Chang

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically.In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them.Index Terms-UAV access point, Flying access point, Drone based user localisation

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Section: Methodsmentioning

confidence: 99%

Autonomous Flying WiFi Access Point

Nunns

Chen

Chang

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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“…Such visual tracking and image matching methods can also be employed for vision-aided localization of UAVs [88]. In addition, a prediction model for the fisher information matrix is developed in [89] for target tracking of moving RF sources.…”

Section: ) Mobile Target Trackingmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

195

100

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Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

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“…By cooperation MDr can use path planning algorithms to produce a coordinated mission, which can increase the detection and tracking chances of ADr flying in their vicinity. Moreover, the cooperative MDr can be fault-tolerant, increase the detection and tracking range, cooperatively localize moving radio frequency source (e.g., ADr) with more accuracy [15], and reduce the interference by using coordinated scheduling. Hence, cooperative schemes is one of the important future research challenge.…”

Section: Cooperative Schemes For Mdrmentioning

confidence: 99%

Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions

Kaleem

Rehmani

2018

IEEE Wireless Commun.

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The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment poses challenges for highly security-sensitive areas such as President house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and flying ad-hoc network (FANET) architectures to assist the efficient deployment of monitoring drones (MDr). To capture amateur drone (ADr), MDr must have the capability to efficiently and timely detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes and also present the limitations in these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for caring of ADr, and intensive research and development is required to fill the gaps in the existing technologies.

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Receding Horizon Multi-UAV Cooperative Tracking of Moving RF Source

Cited by 74 publications

References 10 publications

Autonomous Flying WiFi Access Point

Autonomous Flying WiFi Access Point

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions

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