This study demonstrates improved positioning accuracy using improved whale optimization algorithm in indoor localization, indicating better convergence speed compared to traditional methods.
Indoor visible light localization (VLP) is a research direction for wireless localization due to its high security and abundant spectrum resources. VLP methods based on positional fingerprint localization rely on collecting large amounts of data to achieve localization, which requires cumbersome data collection work and a large amount of data processing work. This paper presents an indoor VLP method based on location fingerprint and an Improved Whale Optimization Algorithm (IWOA), which is divided into offline and online stages. In the offline stage, a relatively small number of signal features are collected in a complex indoor environment and, a fingerprint database is established. To build a realistic noise model, the data in the location fingerprint database are processed by least squares (LS) methods. In the online phase, multilateral measurement is utilized to determine the region of interest (ROI) of the target node (TN). The results show that compared with PSO and WOA, the proposed IWOA improves the convergence speed and positioning accuracy.
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Shi et al. (2025) studied this question.
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