High-Precision Indoor VLP Scheme Based on the Synergy of SMO Multipath Suppression and Intelligent Algorithms

基于SMO多径抑制和智能算法协同作用的高精度室内可见光定位方案

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Abstract

To address the issue that multipath effect severely restricts the performance of indoor visible light positioning (VLP) systems and multipath interference intensity varies significantly across different regions, this paper proposes a spatial adaptive multipath suppression scheme for the first time. At the transmitter, a hybrid transmission architecture of time division multiplexing (TDM) and direct current biased-orthogonal frequency division multiplexing (DCO-OFDM) is employed, providing ideal observation vectors for sparse channel modeling at the receiver through specialized pilot symbol design. At the receiver, a novel Spatial Adaptive-Main Path Energy Constraint-Orthogonal Matching Pursuit (SA-MPEC-OMP, SMO) algorithm is proposed to adapt to the spatial region characteristics with varying multipath intensities, enabling low-latency and accurate separation of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths. Simulation results verify that the SMO algorithm achieves high main path extraction accuracy exceeding 90% in all regions, with its LOS energy ratio 2.7 to 3 times higher than that of the traditional OMP algorithm. Based on the results of the multipath suppression scheme, a high-precision 3D VLP scheme is proposed by integrating the SMO multipath suppression with intelligent algorithms. Specifically, a point classification model performs regional partitioning and dynamic threshold matching, while a height estimation model driven by LOS power extracted via SMO estimates the height of the target point. Finally, 3D coordinates are calculated using trilateration. Simulation results indicate that through the synergy of signal design and algorithm optimization, the proposed scheme achieves centimeter-level positioning across the entire space with a single positioning time of less than 18.7 ms, featuring strong multipath robustness and promising engineering application potential.

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