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Robust Distributed Localization Based on Barycentric Coordinates Under Random Data Loss
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DOI：10.1109/LSP.2025.3625898.png)
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Abstract
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Distributed localization systems enable nodes to determine their locations by exchanging information with neighboring nodes, without relying on centralized infrastructure. While it enhances scalability and robustness, random data loss during information transmission can significantly degrade the localization performance. In this paper, to mitigate the impact of random data loss, we propose a Loss-Robust distributed localization method based on the Distributed Iterative Localization algorithm (LR-DILOC). In LR-DILOC, each node updates its location estimate by leveraging the most recent available location information from its neighboring nodes, thereby improving the utilization of available data. We theoretically analyze the convergence of LR-DILOC, demonstrating that LR-DILOC maintains accurate localization even in the presence of random data loss. Numerical results further validate the theoretical analysis, demonstrating that LR-DILOC achieves higher localization accuracy and exhibits stronger robustness under random data loss.
Keywords:
Barycentric coordinates
distributed localization
random data loss
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