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IAROL: an improved artificial rabbit optimization localization algorithm for occlusion scenarios
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DOI：10.1088/1361-6501/ae61df.png)
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Abstract
En 中文
Ultra-wideband technology has emerged as a leading solution for high-precision indoor localization, owing to its fine time resolution and strong resistance to electromagnetic interference. However, in practical environments, obstacles frequently cause non-line-of-sight propagation, leading to significant ranging errors and degraded positioning accuracy. To address this issue, this paper proposes an improved artificial rabbit optimization localization (IAROL) algorithm based on enhancements to the artificial rabbit optimization. In particular, IAROL introduces an adaptive search space construction strategy that intersects the circumscribed squares of ranging circles to form a compact feasible rectangle, thereby dynamically adjusting the optimization boundary according to real-time geometric constraints. To further improve optimization performance, IAROL integrates Tent map-based population initialization to enhance diversity, Lévy flight to strengthen global exploration, and a modified hidden step-size update formula to accelerate local convergence. Experimental results in an indoor hall and an underground garage demonstrate that IAROL achieves superior localization accuracy and robustness compared to geometric methods such as least square and Chan. Furthermore, when evaluated against mainstream metaheuristic algorithms (including MRSO, IGWO, ASAFMO, and PCCSO), IAROL exhibits competitive accuracy and superior convergence efficiency. Particularly when evaluated with a small population size of 10 and only 40 iterations, IAROL demonstrates exceptional rapid convergence to high-precision solutions, reducing the root mean square error by 5.7%–46.3% and the mean absolute error by 3.0%–42.9% compared to the baseline algorithms across both scenarios.
Keywords:
Ultra-wideband
Indoor localization
Non-line-of-sight
Artificial rabbit optimization
Localization accuracy
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