An inferential system for determination of candidate crash sites for search and rescue operations

Abstract

We describe a methodology for an inferential system for the generation of crash site likelihood maps. These maps assist in the prioritization of candidate aircraft crash sites to be searched by Search and Rescue (SAR) operations. The crash site likelihood maps are created by the fusion of the knowledge and experience of experts in aircraft crash site identification, together with, among others, knowledge about weather conditions, terrain information, and aircraft models. These maps indicate the most likely areas where an aircraft may have crashed, and allow SAR operations to focus their resources in these areas first, leading to faster and more efficient rescue operations. Our approach offers the innovation of the intelligent fusion of a variety of data, information and knowledge sources to generate crash site likelihood maps.