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

Xenakis, Dimitrios; Di Maio, Antonio; Braun, Torsten (2 August 2023). ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization. In: IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2023) (pp. 36-45). IEEE Xplore: IEEE 10.1109/WoWMoM57956.2023.00018

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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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