With the rapid proliferation of the Internet of Things (IoT) and smart devices, the demand for high-precision indoor positioning technologies is increasing. However, traditional positioning methods still face significant limitations in terms of accuracy, cost, and privacy protection. Visible light positioning (VLP) technology, which combines both illumination and positioning capabilities, has become a key research direction in the field of indoor positioning. Nevertheless, in practical applications, the presence or movement of obstacles introduces shadowing effects that severely degrade system accuracy and may cause communication interruptions. These challenges limit the performance improvements and widespread adoption of VLP technology. To address this issue, investigating the influence of obstacles on system performance and developing positioning methods with high adaptability and robustness are critical for both theoretical and practical purposes. In this paper, we propose an innovative indoor visible light positioning method that integrates the war strategy optimization (WSO) algorithm with the generalized regression neural network (GRNN). By optimizing the predictive capabilities of the model, this method significantly enhances positioning accuracy while improving the system’s adaptability and robustness in dynamic environments. The proposed method effectively mitigates the adverse effects of shadowing, offering a practical solution to meet the requirements for high-precision positioning in complex and rapidly changing scenarios.

In this paper, the influence of obstacle position and height on optical power distribution, as well as the effects of shadowing on communication performance and positioning accuracy, are thoroughly analyzed by constructing an indoor visible light channel model. Based on these insights, a WSO-GRNN-based indoor visible light positioning method is proposed. The received signal strength indicator (RSSI) is used as the input feature, and the GRNN smoothness factor is optimized to significantly enhance the performance of the positioning algorithm. A 5 m×5 m×3 m indoor simulation environment is designed, where RSSI data are dynamically collected to build training and test datasets under varying obstacle positions and heights. The positioning performance of the WSO-GRNN method is compared with the least square (LS) method and GRNN through simulation experiments. Key performance metrics, including mean positioning error, root mean square error (RMSE), and the cumulative distribution function (CDF) of errors, are used to evaluate the effectiveness of the algorithms. The experimental results confirm the high adaptability and robustness of the WSO-GRNN algorithm in dynamic environments.

The results of this paper demonstrate that the position and height of obstacles have a pronounced influence on the distribution of received optical power and positioning accuracy. Notably, when obstacles are located in central areas (Fig. 6) or at greater heights (Fig. 9), shadowing effects cause a significant reduction in received optical power, leading to a substantial increase in positioning error (Figs. 7 and 10). Using the proposed WSO-GRNN method, the average positioning error in an obstacle-free environment is less than 7 cm [Figs. 4(g)?(i)], representing a marked improvement compared to LS and GRNN methods. Even in the presence of obstacles, regardless of their position or height, the WSO-GRNN method maintains an average positioning error between 4.1 cm and 7.1 cm, with RMSE ranging from 7.1 cm to 11.2 cm, significantly outperforming GRNN and LS methods (Tables 2 and 3). In addition, WSO-GRNN shows outstanding stability in its CDF results. Even under significant shadowing effects, the error distribution remains within a low range [Figs. 7(c) and 10(c)]. Compared to traditional methods, WSO-GRNN effectively mitigates the adverse effects of shadowing on positioning accuracy, demonstrating high adaptability and robustness in dynamic and complex environments. This method provides an innovative and effective solution to enhance the performance of indoor visible light positioning systems.

Building on the analysis of how obstacles influence indoor visible light positioning systems, we propose the WSO-GRNN positioning method and verify its effectiveness through simulation experiments. The results indicate that the position and height of obstacles significantly influence the distribution of received optical power and positioning accuracy, with shadowing effects being the primary factor contributing to the degradation of communication performance. The proposed WSO-GRNN method achieves a positioning error of only 7 cm in environments without obstacles and maintains high accuracy even in environments with obstacles, with an average error controlled within 8.6 cm, markedly outperforming traditional methods. This method demonstrates strong adaptability and robustness to dynamic environmental changes and effectively mitigates the adverse effects of shadowing on positioning accuracy. However, the current model has limitations in the real-time updating of training data. Future studies could introduce dynamic memory modules or online learning algorithms to enhance the system’s ability to adapt to more complex and rapidly changing real-world environments. This research provides an innovative and effective solution to enhance the accuracy and stability of indoor visible light positioning technology, laying a solid foundation for its application in intelligent buildings, industrial automation, and other fields.