Localization for visible light communication with practical non-Gaussian noise model

In this paper, we propose a localization algorithm in a visible light communication (VLC) system based on the light intensity. Firstly, by developing a VLC platform, the light intensity can be measured to derive a practical equation, which reveals the relationship between the received signal strength (RSS) and the distance from the transmitter to the receiver. However, the practical additive noise in such equation may not always follow the Gaussian distribution, thus, the noise is modeled as a Gaussian mixture model (GMM). Then, a maximum likelihood (ML) estimator is proposed to achieve the optimal localization results. Due to the nonlinearity and nonconvexity, the initial proposed estimator is relaxed into a semidefinite programming (SDP) problem which can be efficiently solved to obtain the globally optimal solution. Finally, experimental results show that the localization accuracy of proposed algorithm outperformances the existing algorithm.

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