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This paper proposes a spatiotemporal coordinated control method for unmanned aerial vehicle (UAV) swarms based on predefined time convergence in response to the coordinated ground attack problem of UAV swarms in a three-dimensional battlefield environment. First, we analyze the UAV swarm’s ground attack task scenario and its relevant elements and establish models for the UAV swarm, ground-moving targets, and attack problems. Building upon problem modeling, this paper considers the spatiotemporal coordinated strategy for a UAV swarm and designs a spatiotemporal coordinated control law for a three-dimensional spatial UAV swarm ground attack. The proposed spatiotemporal coordinated control law can guide the UAV swarm to reach the desired attack position within a specified time, achieving a spatiotemporal coordinated attack on ground-moving targets. A theoretical analysis based on the Lyapunov function demonstrates that the proposed control law can asymptotically stabilize the attack position error variable under a predefined time condition. Simulation results show that the proposed coordinated control law can achieve spatiotemporal coordination of the UAV swarm in a ground-moving target turning motion, enabling the completion of the multiangle coordinated attack on ground-moving targets within a specified time. Currently, research on the coordinated ground attack of UAV swarms mostly focuses on stationary targets, making it difficult to apply to coordinated attack tasks on moving targets. Although the coordinated tracking of moving targets by UAV swarms has been reported in the literature, spatiotemporal coordination among UAVs has not been considered. Therefore, this paper focuses on the spatiotemporal coordinated control strategy of UAV swarms, with a background of coordinated ground attacks by UAV swarms. First, this paper conducts a detailed analysis of the UAV swarm’s coordinated ground attack task in terms of time and multiple elements to establish a comprehensive task profile. Subsequently, by focusing on the core elements of coordinated ground attack by UAV swarms, this paper models both UAV swarms and ground-moving targets. Based on the established models and coordinated control strategy, this paper considers spatiotemporal coordination constraints and designs acceleration commands for each UAV to guide them to reach the specified attack positions at predefined time points, enabling coordinated attacks on the ground targets simultaneously. The designed acceleration control commands serve as inputs to the UAV’s autopilot, further generating lower-level control commands, such as speed, flight path angle, and heading angle, thereby achieving stable flight control of the UAVs. The proposed coordinated control strategy endows the UAV swarm with the capability to achieve spatiotemporal synchronized coordinated attacks, thereby enhancing the operational effectiveness of UAV-coordinated ground attacks.

As an integral part to the air, space and ground integrated network, unmanned aerial vehicle (UAV) swarms have shown broad application prospects in complex tasks such as emergency search and rescue, offensive and defensive confrontation, intelligence reconnaissance and surveillance. The advancements in unmanned technology, coupled with the increasingly rich applications of multi-UAV collaborative intelligence, have engendered new requisites for the information exchange capabilities within UAV swarm networks. Under the umbrella of intent-based network architecture, UAV swarms are capable of autonomously managing the network, thereby mitigating the intricacies inherent in network management. Introducing intent-driven network into UAV swarm systems urgently requires breakthroughs in smart autonomous network technology to enhance the adaptability of intelligent groups in high-dynamic and uncertain environments. A new perspective and method for the collaborative operation and intelligent decision-making of UAV swarms was provided. The architecture, key technologies, and prospects of intent-based UAV swarm networks management were reviewed. Through in-depth analysis and discussion, a solid theoretical foundation and technical reference were aimed to be provided for the future development of UAV swarms.

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Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the main equipment for earthquake disaster rescue and reconnaissance battlefield, the robustness of UAV swarm communication system is an important index of destruction resistance. In this paper, we model the UAV swarm communication system with the help of complex dependency network theory, introduce the relative network effectiveness ratio as an index of the overall robustness of the communication system network, which accurately reflects the interdependence between nodes, and provides a new perspective for evaluating the robustness of the communication system under the target attack. A new perspective is provided for assessing the robustness of communication systems under target attack. Based on the classical M-L model and the load redistribution strategy with dynamic information, we establish the dependent homogeneous ER-ER and heterogeneous ER-BA network models, consider the characteristics of the degree of dependent nodes in the two-layer network for target attack, and analyze the dynamic process of their cascading failure under the attack, as well as the influence of the characteristic parameters, such as the probability of connecting edges of any two nodes, the average degree and the capacity coefficient, on the overall robustness of the communication network. It is shown that network robustness and network dependency strength are negatively correlated, and a reduction of 52.09% to 72.9% dependency strength can be considered in the cascade failure process to resist more serious communication network collapse. For isomorphic networks, the difference in dependency patterns has a greater impact by reducing the overall robustness of RL and AL compared to DL by 9.25% and 15.95%, respectively.

In modern warfare, the use of UAV swarm to deceive and interfere with the hostile networked radar has been widely used as an effective countermeasure. However, when facing a complex combat environment, the digital storage and forwarding equipment carried by the UAV swarm has forwarding delay error due to its limited accuracy. This delay error will lead to a deviation between the generated false target point and the preset false target point to reduce the effectiveness of deception significantly. To address this issue, in given prior knowledge of the location and RF radiation parameters of networked radar stations, this paper proposes a solution based on the concept of “homologous test” and the certain spatial resolution of networked radar. The boundary conditions for effective deception jamming by digital storage and forwarding technology to networked radar are analyzed, and the influence of forwarding delay error on deception jamming effect in typical networked radar systems is identified. Simulation results demonstrate that the analysis and deduction results can provide a reliable assessment of whether the actual jammer performance can effectively deceive and interfere with the networked radar.

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated.Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed.IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP).Based on neighbor’s mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs.Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy.According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability.

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively. The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre-designed rules and heavy computation, and have no ability to generate new rules of the cooperative search. These algorithms belong to the algorithms that can not evolve new rules. Due to the complexity of the mission environment, the algorithms must contain fast,intelligent and robust characteristics, the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability, self-learning ability have been widely concerned. Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environment and task. The paper would systematically analyze and summarize the current research status and progress of cooperative search methods. Finally, the shortcomings of the existing research and the further development are put forward.

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively. The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre-designed rules and heavy computation, and have no ability to generate new rules of the cooperative search. These algorithms belong to the algorithms that can not evolve new rules. Due to the complexity of the mission environment, the algorithms must contain fast,intelligent and robust characteristics, the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability, self-learning ability have been widely concerned. Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environment and task. The paper would systematically analyze and summarize the current research status and progress of cooperative search methods. Finally, the shortcomings of the existing research and the further development are put forward. [ABSTRACT FROM AUTHOR]

Existing routing protocols without awareness of the topology causes excessive retransmissions, energy holes, and long delay, data routing performance was seriously deteriorated. Considering the relation of topology and routing, an intelligent route planning with jointing topology control (IRPJTC) method was proposed. IRPJTC consisted of two part, the virtual force-based adaptive topology control (VFATC), and the PPO-based geographic routing protocol (PPO-GRP). Based on neighbor's mobility information, the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs. Combined with link stability metric in VFATC, end-to-end delay and energy consumption, a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy. According to the performance study, the proposed IRPJTC reduces existing routing protocols by 12.11% of end-to-end delay, and 4.56% of energy consumption, and has a better energy balance ability. [ABSTRACT FROM AUTHOR]

Using Unmanned Aerial Vehicle(UAV) swarm to deal with emergencies is an efficient handling mode. In order to establish a comprehensive security prevention and control system from the ground to the air, from the plane to the three-dimensional, the UAV swarm emergency response system model for emergencies is built by using the Department of Defense Architecture Framework(DoDAF), and the system structure, task activities, organizational relationships, ability indicators and other models of the system are established based on the task requirements. With AHP(Analytic Hierarchy Process)and fuzzy synthetic evaluation method, the capability requirements are analyzes combining qualitative and quantitative methods. The analysis provides support and research perspectives for the application requirements and demonstration for UAV emergency warfare.

In order to meet the demands of dual-band and integrated conformal antenna for UAV (Unmanned Aerial Vehicle) swarm communication systems, an integrated wing conformal antenna is designed and fabricated. The antenna is realized by combining a broadband dipole antenna and a low pass and high-pass LC combiner. The antenna element is realized by three pairs of open branch lines to achieve effective choking characteristics and broad- band omnidirectional radiation. The combiner consists of K-m LC filters, which are used to realize dual - band operation with low insertion loss and high rejection. The measured Voltage Standing Wave Ratio(VSWR) of antenna is less than 2, the measured gain is more than 0 dBi, and the isolation is better than 30 dBc between L and S frequency band. By mounting only one wing conformal antenna, a dual-band simultaneous operation can be realized which meets the need of UAV swarm data link system for high communication rate. The proposed antenna is whole arranged in the tail wing of UAV to enhance the integration capability of the UAV swarm, and can be widely used in UAV field. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the performance of unmanned aerial vehicles and the extensive use of tactical methods, the cooperative operation of unmanned aerial vehicles (UAV) swarm has brought new challenges to the current air defense system. By combing and analyzing the application cases of UAV swarm warfare in recent years, this paper summarizes the characteristics of current UAV swarm warfare application, and explains the necessity and urgency of developing anti UAV swarm warfare command and control system. In addition, this paper analyzes the current situation of the command and control system of anti UAV swarm warfare in the world's military powers, and on this basis, puts forward some enlightenment suggestions for the development of the future anti UAV swarm warfare command and control system.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous improvement of the performance of unmanned aerial vehicles and the extensive use of tactical methods, the cooperative operation of unmanned aerial vehicles (UAV) swarm has brought new challenges to the current air defense system. By combing and analyzing the application cases of UAV swarm warfare in recent years, this paper summarizes the characteristics of current UAV swarm warfare application, and explains the necessity and urgency of developing anti UAV swarm warfare command and control system. In addition, this paper analyzes the current situation of the command and control system of anti UAV swarm warfare in the world’s military powers, and on this basis, puts forward some enlightenment suggestions for the development of the future anti UAV swarm warfare command and control system. [ABSTRACT FROM AUTHOR]

To reduce the communication complexity and improve the robustness of blockchain network, a two-layer grouped Byzantine fault tolerance algorithm for unmanned ariel vehicle (UAV) swarm was proposed.First, nodes were divided into several groups according to the formation results of UAV swarm.A portion of high-reputation nodes were selected from each group to form a committee, and other nodes were used as shared nodes.Threshold signature techniques were employed to reduce communication complexity.In that way, nodes from other groups only needed to communicate with the committee nodes of the current group, thus greatly reducing the average communication delay.In addition, the hierarchical structure, which made the identity of the primary node hidden in the committee, reduced the risk of being adaptively attacked by the Byzantine nodes and improves the robustness of the system.Experimental results demonstrates that the proposed algorithm significantly reduces the delay of the consensus process and can effectively ensure the aliveness of the system compared with the comparison algorithm.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agent-based combat modelling and simulation is an important method for UAV swarm surveillance effectiveness evaluation. The agent behaviour mechanisms and model structures in previous agent-based combat modelling and simulation studies are difficult to describe self-organized surveillance behaviours in complex mission scenarios. Taking a counter-piracy surveillance mission as an example, a combat modelling and simulation method oriented for swarm self-organized surveillance is proposed. By the hierarchical composite behaviour mechanism and the composable modular model structure, complex behaviours are described by the composition of simple behaviours, and the self-organized path planning and flight control are realized by the interaction of the pheromone map module and the path management module. Simulation experiments are conducted to demonstrate the feasibility of the proposed method, and compare the surveillance effectiveness with different path planning methods. The results show that, self-organized path planning is more adaptive to dynamic events interrupting surveillance. In large-area, long-duration and low-target-density area coverage surveillance missions, merely decreasing revisit intervals by improving path planning methods cannot significantly increase the probability of finding targets.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)