室内射线跟踪关于反射次数的收敛性分析

由于射线跟踪所需时间随着反射次数增加而迅速增加，反射次数的上限值对于室内射线跟踪的精确度和效率至关重要。因此，本文开展了一个基于统计分析室内场景数据的射线跟踪收敛性研究。通过统计场景中的分布特征，分析射线跟踪不同反射路径的概率，研究接收功率关于射线跟踪最大允许反射次数的收敛关系，得出最优的反射次数上限值，实现优化室内射线跟踪的效率和精确度。将本文模型收敛性与射线跟踪仿真结果的收敛性参数进行对比，在同样计算精确度下，本文模型的收敛结果与射线跟踪仿真收敛结果一致，验证了本文模型的准确性。

Convergence analysis on reflection in indoor 3D ray tracing

Computational time in indoor Ray Tracing(RT) would rise sharply if more reflections are considered. The upper limit value of reflection number is the key to the efficiency of indoor RT. However, the convergence of reflection in indoor RT has not been clearly discussed yet. A convergence analysis of RT based on statistically studying digital data of indoor scenario is presented aiming to obtain the appropriate upper limit value of reflection number to improve the efficiency of RT without losing accuracy. Convergence study focuses on the distribution of indoor scene, derives the probabilities and the power of different reflection paths, and finally finds out the convergence result of the power of receiver and the upper limit value of reflection number. The comparisons with RT simulation show that the convergence of this model is consistent with that of RT simulation under the same computational accuracy and this study is feasible to improve the efficiency of RT.