Flipping Free Conditions and Their Application in Sparse Network Localization

Ping, Haodi; Wang, Yongcai; Li, Deying; Sun, Tianyuan

doi:10.1109/tmc.2020.3015480

Cited by 9 publications

(3 citation statements)

References 64 publications

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“…The geographical locations of nodes are fundamental information for many multi-agent applications [10], [11], [12]. Network localization techniques are usually adopted for calculating node locations in infrastructure-less scenarios [13], [14], which are formulated as follows. For a network of m + n agents in R d , let V = A ∪ F denote the entire node set.…”

Section: B the Network Localization Problemmentioning

confidence: 99%

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

Ping¹,

Wang²,

Li³

2021

Preprint

Self Cite

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Distributed algorithms to solve linear equations in multi-agent networks have attracted great research attention and many iteration-based distributed algorithms have been developed. The convergence speed is a key factor to be considered for distributed algorithms, and it is shown dependent on the spectral radius of the iteration matrix. However, the iteration matrix is determined by the network structure and is hardly pre-tuned, making the iterative-based distributed algorithms may converge very slowly when the spectral radius is close to 1. In contrast, in centralized optimization, the Conjugate Gradient (CG) is a widely adopted idea to speed up the convergence of the centralized solvers, which can guarantee convergence in fixed steps. In this paper, we propose a general distributed implementation of CG, called DCG. DCG only needs local communication and local computation, while inheriting the characteristic of fast convergence. DCG guarantees to converge in 4Hn rounds, where H is the maximum hop number of the network and n is the number of nodes. We present the applications of DCG in solving the least square problem and network localization problem. The results show the convergence speed of DCG is three orders of magnitude faster than the widely used Richardson iteration method.

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Section: B the Network Localization Problemmentioning

confidence: 99%

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

Ping¹,

Wang²,

Li³

2021

Preprint

Self Cite

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show abstract

“…At the same time, CL systems remain highly susceptible to flip-ambiguities. Viewing the system as a graph, rigidity theory shows that flip-ambiguity occurs when a sub-graph flips across an axis without edge constraints violations [1]. In other words, when sub-groups of correctly positioned nodes (in the subgroup) are connected with other such sub-groups, incorrectly.…”

Section: Introductionmentioning

confidence: 99%

“…3) To facilitate the reproduction of our results and future comparisons, we openly release ARLCL source code 1 .…”

Section: Introductionmentioning

confidence: 99%

ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization

Xenakis

Maio

Braun

2023

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Positioning estimations of wireless sensors can be enhanced via sensor collaboration. To enable this, various methods have been proposed; yet, most do not leverage the entire collective knowledge, which also involves the estimation's uncertainty. In this article, we introduce Anchor-free Ranging-Likelihood-based Cooperative Localization (ARLCL); a novel anchor-free and technology-agnostic localization algorithm that utilizes inter-exchanged ranging signals from sensors to enable their simultaneous positioning. Ranging technologies with easyto-model propagation properties, such as UWB or LiDAR are among the first beneficiaries that ARLCL is targeting. To examine its applicability, however, even to signals that are noisier and often unsuitable for ranging, we assess ARLCL with real-world BLE RSS measurements. At the same time, we consider deployments that typically induce flip-ambiguity, being a major problem in cooperative localization. We provide an extensive comparison against the most widely-adopted optimization method (Mass-Spring) but also against the recent likelihood-based approach (Maximum Likelihood -Particle Swarm Optimization). The results showed that ARLCL outperformed the baselines in almost all scenarios. Our gain in positioning accuracy is also found to be positively correlated to both the swarm's size and the signal's quality, reaching an improvement of 40%.

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Communication Efficient, Distributed Relative State Estimation in UAV Networks

Wang

Bai

et al. 2023

IEEE J. Select. Areas Commun.

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Flipping Free Conditions and Their Application in Sparse Network Localization

Cited by 9 publications

References 64 publications

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

DCG: Distributed Conjugate Gradient for Efficient Linear Equations Solving

ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization

Communication Efficient, Distributed Relative State Estimation in UAV Networks

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