Decentralised Multi-UAV Cooperative Searching Multi-Target in Cluttered and GPS-Denied Environments

Xiaolong, Zhu; Alvarez, Fernando Vanegas; González, Luis F.

doi:10.1109/aero53065.2022.9843665

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…This means (y Ψ , R Ψ ) is uncorrelated with previously transmitted information which is crucial for using KF to fuse (y 1 , R 2 ) with (y Ψ , R Ψ ). However, since (γ, Γ) 3 The source file is named example_cie_convergence.m. is not equivalent to (y 1 , R 1 ), the computed Ψ is in general suboptimal.…”

Section: Discussionmentioning

confidence: 99%

“…We will now examine convergence properties of Γ using a simple example. MATLAB ® code 3 for the example is available at https: //gitlab.com/robinforsling/dtt/. Let x 0 ∈ R 2 be stationary.…”

Section: Convergence Analysismentioning

confidence: 99%

“…Data fusion in decentralized sensor networks (SNs) is an active research field. Common applications include multisensor target tracking [1,2], localization [3] and robotics [4,5]. One main challenge is proper handling of crosscorrelations between estimates to be fused.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Data Fusion of Dimension-Reduced Estimates Using Local Information Only

Forsling

Gustafsson

Sjanic

et al. 2023

2023 IEEE Aerospace Conference

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This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small.

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Section: Discussionmentioning

confidence: 99%

Section: Convergence Analysismentioning

confidence: 99%