Indoor Visible Light Fingerprint Location Method Based on Marine Predator Algorithm-Optimized Least Squares Support Vector Machine

To increase the accuracy of indoor visible light positioning, a novel indoor visible light localization technique based on the marine predator algorithm-optimized least squares support vector machine (MPA-LSSVM) is suggested. The light signals of each reference point are recorded in the first place and a fingerprint database is created. Introduced thereafter is the marine predator algorithm, which, through iterative optimization of the hyperparameters of the least squares support vector machine, aims to establish an optimal localization model using finely-tuned hyperparameters. This culminated in the development of a positioning model, successfully attaining the objective of enhancing accuracy in positioning while minimizing time expenditure. In an indoor-positioning scene (size: 1 m × 1 m × 1 m), the average positioning error of the proposed positioning method is 0.041 m, and the proportion of test points with positioning errors less than 0.1 m is 96.7%.