Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Koohifar, Farshad; Güvenç, İsmail; Sichitiu, Mihail L.

doi:10.1109/access.2018.2810599

Cited by 63 publications

(44 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the field of electromagnetic spectrum monitoring, to monitor private black broadcasts, a group of unmanned aerial vehicles carrying sensors that receive strong signals are used to locate intermittently launched RF transmitters [139]. [140] emphasize the importance of UAVs in agriculture, by using UAVs in agricultural monitoring and observation to increase crop yields.…”

Section: C) Electromagnetic Spectrum Monitoring Fieldmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

show abstract

Section: C) Electromagnetic Spectrum Monitoring Fieldmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

View full text Add to dashboard Cite

show abstract

“…For offline and online search operations, a framework was proposed in Yin et al 114 These static obstacles were captured in offline and dynamic obstacles in online. To track the radio frequency resources steepest descent was proposed in Koohifar et al 115 Moreover, bio‐inspired models were used for the target position of UAVs. For optimization of routing planning of UAVs, optimization based on mobile users was proposed in Jeong et al 116 To minimize the intelligence, surveillance two echelon‐optimization algorithms for UGVs and UAVs were proposed in Luo et al 117 Under UAVs routing planning, much research work has been done.…”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

View full text Add to dashboard Cite

SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

show abstract

“…This will require a much higher degree of network flexibility than in the past in order to smoothly and autonomously react to fast temporal and spatial variations of traffic demand. At the same time, the idea of having swarms of UAVs being accepted by the wide public might be challenging because of the possibility of their malicious use [8], [9]. In fact, an important problem is the possible presence of sinister UAVs that can hide behind buildings for illegal activities, e.g., terrorist attacks, or can blind UAV swarms to inhibit their functionality.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

2020

View full text Add to dashboard Cite

Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

show abstract

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Cited by 63 publications

References 28 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Contact Info

Product

Resources

About