=== Pros and Cons of EM.Terrano's SBR Solver ===
[[EM.TErranoTerrano]]'s SBR simulation engine utilizes an intelligent ray tracing algorithm that is based on the concept of k-dimensional trees. A k-d tree is a space-partitioning data structure for organizing points in a k-dimensional space. k-d trees are particularly useful for searches that involve multidimensional search keys such as range searches and nearest neighbor searches. In a typical large radio propagation scene, there might be a large number of rays emanating from the transmitter that may never hit any obstacles. For example, upward-looking rays in an urban propagation scene quickly exit the computational domain. Rays that hit obstacles on their path, on the other hand, generate new reflected and transmitted rays. The k-d tree algorithm traces all these rays systematically in a very fast and efficient manner. Another major advantage of k-d trees is the fast processing of multi-transmitters scenes. Unlike the previous versions of the SBR solver which could handle one transmitter at a time and would superpose all the resulting rays at the end of the simulation, the new SBR shoots rays from all the transmitters at the same time.
[[EM.Terrano]]'s SBR solver performs fully polarimetric and coherent SBR simulations with arbitrary transmitter antenna patterns. The new engine solves directly for the vectorial field components at the receiver locations or field observation points. This is far more rigorous than the previous versions of the SBR solver which primarily utilized ray power calculations based on the two vertical and horizontal polarizations. In other words, [[EM.Cube]]'s new SBR engine is a truly asymptotic "field" solver. As a result, you can visualize the magnitude and phase of all six electric and magnetic field components at any point in the computational domain. For power calculations at the receiver location, an isotropic, polarization-matched, receiving antenna is assumed.