Enhanced Heuristic GWO for High-Accuracy Indoor VLP by Fusing RSS and AoA

Conventional visible light positioning (VLP) systems are limited by inadequate positioning accuracy and vulnerability to obstacle occlusion, thereby hindering their deployment in precision-critical applications. To address these challenges, this paper proposes a fusion algorithm that synergistically combines received signal strength (RSS) and angle of arrival (AoA) information. Furthermore, the proposed approach incorporates an intelligent reflecting surface (IRS) framework into the system model, thereby improving system robustness and simultaneously enhancing positioning accuracy under sparse light-emitting diode (LED) deployment, blockage, or non-line-of-sight (NLoS) conditions. Specifically, this paper employs a multi-photodetector (PD) array at the receiver to formulate a system of linear equations based on RSS measurements, which facilitates accurate angle estimation. This derived AoA information is subsequently fused with the RSS data to establish a joint positioning objective function, thereby mitigating the limitations associated with single-parameter approaches. Crucially, an optical IRS is integrated to produce robust NLoS propagation paths, significantly enhancing accuracy in scenarios characterized by a scarcity of LEDs or obstructed line-of-sight (LoS) links, which are common challenges in practical deployments. To address the resulting non-convex optimization problem, a dimension learning-based hunting enhanced grey wolf optimizer (GWO-DLH) is developed, ensuring efficient convergence to the global optimum. Comprehensive simulations conducted under realistic channel models demonstrate that the proposed algorithm achieves a lower root-mean-square error compared to conventional RSS-only or AoA-only methods, while maintaining a computational complexity that is comparable to state-of-the-art techniques. These findings substantiate the algorithm’s effectiveness in balancing accuracy and robustness, thereby providing a foundational framework for the advancement of high-precision indoor optical positioning systems.