Consensus-Based Multi-UAV Target Tracking with Communication Delays

Xiaohang, Shi; Bi, Bo; Zhang, Qingjie; Sun, Chao; Lv, Junwei

doi:10.1109/ihmsc.2017.187

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…With the development of modern unmanned technology, unmanned aerial vehicles (UAVs) have been extensively utilized in compelling various applications, as hazard warning, border patrol, and surveillance relying on their pleasant features of convenient deployment and low cost [1,2]. Especially in recent years, tasks such as target detection, tracking and positioning based on UAV swarms [3,4] have become hot topics in the field of cooperative control [5−11].…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Detection Game: UAV Swarm vs. One Fast Intruder

Xiao

2023

J. of Syst. Eng. Electron.

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This paper studies a special defense game using unmanned aerial vehicle (UAV) swarm against a fast intruder. The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination. As defenders, the UAVs are arranged into three layers: the forward layer, the midfield layer and the back layer. The co-defense mechanism, including the role derivation method of UAV swarm and a guidance law based on the co-defense front point, is introduced for UAV swarm to co-detect the intruder. Besides, five formations are designed for comparative analysis when ten UAVs are applied. Through Monte Carlo experiments and ablation experiment, the effectiveness of the proposed co-defense method has been verified.

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Section: Introductionmentioning

confidence: 99%

A Cooperative Detection Game: UAV Swarm vs. One Fast Intruder

Xiao

2023

J. of Syst. Eng. Electron.

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“…Furthermore, the convergence of algorithm is accelerated by using two-hop paths [15], stored memory [16], prediction functions [17], buffer-aided relays [18], or by including non-linear terms [19]. In addition, to adapt to practical scenarios, further resource savings can be achieved by setting iteration threshold parameters [20,21] and dividing the subnetwork [22], using information compensation to solve the communication delay problem [23], using system noise to solve the node failure problem [24], and using subgradients to solve the asynchronous network problem [25]. The average consensus algorithm is used to implement the multi-UAV network channel rendezvous algorithms proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Channel Rendezvous Algorithm for Multi-Unmanned Aerial Vehicle Networks Based on Average Consensus

Wang,

Zhang,

Qin

et al. 2023

Sensors

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Realizing the distributed adaptive network construction of multi-UAV networks is an urgent challenge, as they lack a reliable common control channel and can only maintain a limited sensing range in crowded electromagnetic environments. Multi-unmanned aerial vehicle (UAV) networks are gaining popularity in many fields. In order to address these issues, this paper proposes a multi-UAV network channel rendezvous algorithm based on average consistency. The goal of the algorithm is to adjust the communication channels of each UAV to converge on the same channel, since the communication link of the multi-UAV network is broken due to interference. The proposed memory-based average consistency (MAC) algorithm utilizes the network adjacency matrix as prior information. Furthermore, for the case where the adjacency matrix is unknown, this paper also proposes the Multi-Radio Average Consensus (MRAC) algorithm, which achieves a beneficial trade-off between rendezvous performance and hardware cost. Simulation results demonstrate that the proposed MAC and MRAC algorithms provide superior network convergence time and scalability in networks of different densities. Finally, a hardware simulation platform based on a multi-UAV network was designed using a software-defined radio platform, and experimental simulations were performed to prove the effectiveness of the MAC algorithm in a real environment.

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“…There exist several and diverse applications for MAS consensus control, for instead in (WANG; DING; SHENG, 2014) the consensus control of multiple networked robots with input delays are implemented and validated by experimental results, in (ZHONGHE et al, 2013) a feedback controller is designed to reduce the traffic congestion in urban road traffic networks, the control of energy storage in microgrids is addressed in (KHAZAEI; MIAO, 2016) to synchronize the state-of-charge (SoC) and power levels of batteries with limited information exchange, in (GÜZEY; DUMLU, 2018) the nonlinear consensus synchronizing control for networked DC motors to tracking a desired position or velocity is developed, also, the consensus control of MAS is continuously applied in the distributed control of unmanned vehicle groups (SHI et al, 2017;ZHANG et al, 2019;KADA;KHALID;SHAIKH, 2020).…”

Section: Mas Consensus Controlmentioning

confidence: 99%

Formation static output control of linear multi-agent systems with hidden Markov switching network topologies.

Canales¹

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Esta dissertação aborda o controle estático de saída de formação H 2 , H ∞ e H 2 /H ∞ misto para sistemas lineares multiagentes a tempo contínuo com topologias de rede de comutação Markoviana. Supõe-se que o modo de operação da topologia da rede não pode ser medido diretamente, mas em vez disso, pode ser estimado por um detector imperfeito. Para modelar este problema consideramos um modelo de Markov oculto em tempo contínuo, no qual o componente oculto representa o modo real de operação da topologia da rede enquanto o componente observado representa a informação emitida pelo detector e disponível para o controlador. Também é assumido que apenas uma informação parcial das variáveis de estado dos sistemas multiagente está disponível. Usando uma formulação LMI (linear matrix inequality), um controlador de saída estático distribuído que muda de acordo com as informações do detector é projetado para garantir a estabilidade no sentido da média quadrática do sistema de malha fechada, bem como um limite superior para um índice de desempenho. Três situações são consideradas para os critérios de desempenho: a norma H 2 , a norma H ∞ e a norma mista H 2 /H ∞ . O trabalho é concluído com exemplos numéricos para ilustrar a eficácia dos resultados teóricos.Palavras-Chave -Controle de formação H 2 , H ∞ e H 2 /H ∞ misto, topologias de comutação Markoviana, desigualdades matriciais lineares, sistema linear multiagente, controle estático de saída.

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Consensus-Based Multi-UAV Target Tracking with Communication Delays

Cited by 6 publications

References 10 publications

A Cooperative Detection Game: UAV Swarm vs. One Fast Intruder

A Cooperative Detection Game: UAV Swarm vs. One Fast Intruder

A Channel Rendezvous Algorithm for Multi-Unmanned Aerial Vehicle Networks Based on Average Consensus

Formation static output control of linear multi-agent systems with hidden Markov switching network topologies.

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