A MATLAB©-based vehicular visible light communication (VLC) simulator for vehicle localization via visible light positioning (VLP). The simulator supports the following article under review:
B. Soner, S. Coleri, "Visible Light Communication based Vehicle Localization for Collision Avoidance and Platooning".
Update (09.09.2021): Addressing the following sentence from Section V-3: "Simulated accuracy meets the CRLB as shown in Fig. 11e, demonstrating that the proposed VLP algorithm is an efficient estimator, i.e., the minimum variance unbiased estimator for the dual-AoA vehicular VLP problem". We've added a more detailed statistical analysis of our estimator and observation model to "99_docs/crlb_analysis_mvuproof.pdf", in which we show that the while the estimator is not efficient in the CRLB sense, it is the best possible estimator for this observation model in terms of mean squared error, i.e., it is the minimum variance unbiased (MVU) estimator for this problem. We prove this via applying the Rao-Blackwell-Lehmann-Scheffé (RBLS) theorem to our problem. Furthermore, we also show that our estimator also produces the maximum likelihood estimates (MLE) for this problem.
VLC channel simulation is radiometric and assumes LoS communication. VLC units are vehicle head/tail lights consisting of LED lights as transmitters and custom angle-of-arrival-sensing receivers, named QRX. The angle-of-arrival on the QRXs are calculated and are used for localization and pose estimation. Vehicle trajectories are either generated using the well-known microscopic traffic simulator SUMO or generated manually using a custom MATLAB© script.
The main components are given below. Each component is configured via a script, and is documented in place.
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VLC Configuration Tool (00_vlcCfg) (QRX is the novel receiver proposed in the article, the schematics for the prototype will be provided in the future)
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Vehicle Trajectory Configuration Tool (01_vehCfg)
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Vehicular VLC Simulation (02_v2lcDataGen)
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Simulations for the Localization Algorithm (03_simulations) (the novel algorithm proposed in the article)
QRX:
System Model:
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To regenerate the simulation figures in the article: run the corresponding .m file under "03_simulations/". These .m files correspond to the simulation scenarios presented in the article. Check the "sim" and "res" flags inside: Sim re-generates algorithm simulation results, res just prints results with previously generated simulation results.
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To re-generate simulation data for an example trajectory and an example VLC configuration: run "02_v2lcDataGen/v2lcDataGen.m" and choose 1-which VLC config you want, 2-which trajectory you want. Each trajectory corresponds to a simulation scenario in the article but the default VLC configuration (corresponding to a typical vehicle tail-light) is used for all scenarios.
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To edit the VLC or vehicle trajectory configurations and re-run the whole simulation process from scratch: follow the guidelines under each folder.