Combined approach to tactical situations analysis

Abstract

Analysis of available sources reveals that situation awareness (SAW) for various objectives is realized through different approaches and techniques. Situation awareness is the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. The research done showed that results received in the field of artificial intelligence could be used to elaborate real SAW work algorithms. The most developed fields here are artificial neural networks (ANN) and genetic algorithms (GA). New progress and research in informatics, based on information processing implementing protein molecules' immune networks, processing principles appeared under the term of "immunocomputing" (IC). However, precision of situation recognition when using IC method directly depends on the training sample volume and quality, as it happens when using any other pattern recognition method. And yet during analysis it is possible that training data volume could be evidently insufficient for the correct situation recognition. In that case Aggregated Indices Method (AIM) could be used for the situation analysis. In the context of this method it is assumed that the experts assess the factors defining the tactical situation. In the method's framework any process of alternatives' preference estimation by an aggregated preference index may be put into terminological shape of correspondent objects quality estimation by an aggregated quality index. In this paper the combined approach to the tactical situation analysis is offered. The mentioned methods application in the uniform situation analysis system with the purpose of the recognition reliability rate increase is examined.