Abstract:Large-scale unmanned aerial vehicle (UAV) formations are vulnerable to disintegration under electromagnetic interference and fire attacks. To address this issue, this work proposed a distributed formation method of UAVs based on the 3 × 3 magic square and the chain rules of visual reference. Enlightened by the biomimetic idea of the plane formation of starling flocks, this method adopts the technical means of airborne vision and a cooperative target. The topological structure of the formation’s visual referenc… Show more
“…In the case of multiple vehicles, distributed cooperative control laws are proposed in [10] for the problem of interception of static and maneuvering targets by several UAVs. In [11], a distributed formation controller is presented using specific index patterns and chain rules of visual references among the vehicles of the fleet, resulting in a good robustness wrt losses of vehicle(s).…”
Unmanned Aerial Vehicles (UAVs) are recognized as very useful tools to replace, help, or assist humans in various missions, such as inspection and monitoring, surveillance, search and rescue, exploration, logistics and transportation, etc [...]
“…In the case of multiple vehicles, distributed cooperative control laws are proposed in [10] for the problem of interception of static and maneuvering targets by several UAVs. In [11], a distributed formation controller is presented using specific index patterns and chain rules of visual references among the vehicles of the fleet, resulting in a good robustness wrt losses of vehicle(s).…”
Unmanned Aerial Vehicles (UAVs) are recognized as very useful tools to replace, help, or assist humans in various missions, such as inspection and monitoring, surveillance, search and rescue, exploration, logistics and transportation, etc [...]
“…In this case, multiple relay UAVs need to be deployed in the path to form a swarm network in the sky by establishing an UAV-to-UAV link (UAV) between numerous source nodes and ground control stations. With its flexible mobility and strong battlefield adaptability, UAVs have been widely used in various fields 9 . performance of UAVs was remarkable, and UAVs may become the main force of future warfare [10][11][12][13] .…”
In complex environments, UAV wireless sensing networks suffer from path loss, incomplete channels, swarm access security and other problems, and the signal fading and packet loss rates are very obvious. In order to solve the problem of user control data security, this paper gives full play to the characteristics of blockchain technology and builds a blockchain-based UAV cluster anti-interference communication network in UAV cluster applications to achieve antiinterference performance. Meanwhile, Markov decision process is used to optimize and realize the research of UAV airground integrated radio wave anti-interference.
Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.
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