Acceleration of Shadowing Detection with Octree and Improved Specular Model for Indoor Propagation Using Point Cloud Data

Abstract

An efficient shadowing detection method based on octree and an improved specular model for indoor propagation using point cloud data is presented. By partitioning the overall space containing all points into the leaf nodes, an octree can shrink the traverse range dramatically. The theoretical value of the projected Fresnel area is applied to promote the specular model. Numerical results show that the octree has an excellent performance with a high accelerate rate. And The comparisons with ray tracing (RT) and the original method demonstrate that the improved specular model can indeed provide more precise results for indoor propagation. Combined with these two methods, a good simulation result can still be obtained even in a realistic complex indoor environment.