Implementing a Probabilistic Line of Sight in EASEE (Environmental Awareness for Sensor and Emitter Employment).

摘要

This report covers the effort to better represent the effects of natural and man-made surface features on visibility by incorporating probabilistic methods into a line-of-sight tool within the Environmental Awareness for Sensor and Emitter Employment (EASEE) software package. Traditional line-of-sight methods are strictly binary: the possibility of seeing from one point to another is either yes or no without a consideration for what is in-between. The major issue that hinders traditional line-of-sight tools is that they use only one elevation model in their calculations. A single elevation model oversimplifies the complexity of the physical environment, creating unrealistic representations of both the Earth's surface and visibility. While computationally fast, this type of tool results in output that is often conceptually flawed. The developed probabilistic line-of-sight algorithm corrects for this issue by incorporating multiple elevation models and land cover data to better represent and characterize features present on the Earth's surface. The probabilistic line of sight is able to calculate the likelihood of attenuated visibility based on the classification and dimensions of obscurants along the path between observer and target.