Robust joint optical delay and received signal strength positioning for visible light communication systems using particle swarm optimization.

Accurate indoor positioning in Global Navigation Satellite Systems (GNSS)-denied environments remains a critical challenge for next-generation intelligent spaces, industrial automation, and context-aware services. Visible light communication (VLC) and visible light positioning (VLP) have emerged as attractive solutions due to their dual functionality of illumination and communication, immunity to electromagnetic interference, and potential for high-accuracy localization. However, practical VLC/VLP systems are highly sensitive to receiver orientation, LED/front-end nonlinearities, ambient light, multipath reflections, and hardware-related timing offsets and RSS measurement distortions. In this paper, we propose a robust joint optical delay-received signal strength (RSS) positioning framework for VLC systems, where delay-derived pseudorange information and received optical power measurements are fused under a unified weighted negative log-likelihood formulation. Unlike conventional VLP schemes that rely on either RSS-only or pseudorange-only inference under Gaussian assumptions, the proposed approach explicitly accounts for non-Gaussian measurement distortions and per-link timing bias in the optical domain. The user position and per-link timing-bias parameters are jointly estimated through particle swarm optimization (PSO), enabling robust operation in highly nonlinear and multimodal VLC localization landscapes. In the evaluated LOS-dominant scenario, the proposed joint optical delay-RSS estimator achieves an RMSE of [Formula: see text] and a 95th percentile error of [Formula: see text], outperforming the Optical pseudorange WNLS, RSS-only Lambertian WNLS, and Joint pseudorange-RSS (PSO + Huber) baselines. This framework provides a calibration-efficient and Lambertian-consistent solution for indoor optical positioning and constitutes a promising basis for accurate localization in smart buildings, hospitals, warehouses, and industrial environments. These results should be interpreted within the simulation-based scope of the present study, and experimental validation remains necessary before drawing stronger deployment-oriented conclusions.