Distributed Particle Filter Based on Particle Exchanges

Tang, Rui; Riemens, Ellen; Rajan, Raj Thilak

doi:10.1109/aero55745.2023.10115781

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…In case of UAVs, advanced neural networks, e.g., Long Short-Term Memory [83,84] have recently shown promising results for accurately tracking the agent trajectory. In the future, for a network of UAVs, distributed state-space models can be employed for reliable tracking of the state variables, e.g., distributed Kalman filtering [85,86] or distributed particle filtering [87,88].…”

Section: Robust Spatio-temporal Awareness Of Uav Swarmsmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.

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Section: Robust Spatio-temporal Awareness Of Uav Swarmsmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

Distributed state estimation with compressed and synchronized auxiliary particle filters using graph theory

Maghsudlu,

Danaee,

Arezumand

2024

Discov Electron

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In this paper, we propose a novel compressed distributed auxiliary particle filter that uses graph theory (CDAPF-GT) to reduce the communication cost and improve the estimation accuracy of a nonlinear state space model. Our method compresses the global loglikelihood function into a set of parameters that are updated by an average consensus algorithm over a network of nodes. Unlike the existing methods, our method synchronizes the particle sets among all the nodes and uses the latest measurements to construct a better proposal distribution. Our method is suitable for applications that require fast and reliable distributed state estimation. We show through various simulation scenarios that our method outperforms the common counterpart method in terms of estimation accuracy with the same level of communication.

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Distributed Particle Filter Based on Particle Exchanges

Cited by 2 publications

References 23 publications

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Distributed state estimation with compressed and synchronized auxiliary particle filters using graph theory

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