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This paper proposes a lightweight bidirectional scattering distribution function (BSDF) model for layered materials with anisotropic reflection and refraction properties. In our method, each layer of the materials can be described by a microfacet BSDF using an anisotropic normal distribution function (NDF). Furthermore, the NDFs of layers can be defined on tangent vector fields, which differ from layer to layer. Our method is based on a previous study in which isotropic BSDFs are approximated by projecting them onto base planes. However, the adequateness of this previous work has not been well investigated for anisotropic BSDFs. In this paper, we demonstrate that the projection is also applicable to anisotropic BSDFs and that the BSDFs are approximated by elliptical distributions using covariance matrices.

Here, we propose a model to determine the effect of multipath in indoor environments when the shape and characteristics of the environment are known. The main paper goal is to model the multipath signal formation to solve, as much as possible, the negative effects in light communications, as well as the indoor positioning errors due to this phenomenon when using optical signals. The methodology followed was: analyze the multipath phenomenon, establish a theoretical approach and propose different models to characterize the behavior of the channel, emitter and receiver. The channel impulse response and received signal strength are obtained from different proposed algorithms. We also propose steps for implementing a numerical procedure to calculate the effects of these multipaths using information that characterizes the environment, transmitter and receiver and their corresponding positions. In addition, the results of an empirical test in a controlled environment are compared with those obtained using the model, in order to validate the latter. The results may largely vary with respect to the cell size used to discretize the environment. We have concluded that a cell size whose side is 20-times smaller than the minimum distance between emitter and receiver (i.e., 10 cm × 10 cm for a 2-m distance) provides almost identical results between the empirical tests and the proposed model, with an affordable computational load.