The methodology and algorithms of aerial object classification by the decision support system under intense information influence
The paper considers the development of methodology and algorithms for classification of aerial objects by a decision support system of the automated control system under intensive information influence. In case of such influence, the signs of system information overload are the amount of data on the air situation (marks from aerial objects) and the information quality. When solving the problem of object classification according to the catalog under parametric uncertainty and class intersection, the use of the sequential normalization procedure based on the entropy maximization principle allows obtaining the least doubtful distribution of probabilities of classifying each of the objects to known or new classes. The paper proposes a combination of the basic method with the basic principles of the methods of split up standards and cluster analysis. The combination allows improving classification characteristics. The basis of the developed methodology of aerial objects classification by a decision support system under intensive information influence is the introduction of the concept of parametric space into the basic method, as well as the representation of classification objects and classes from the catalog of reference values. At the same time, each of the main stages of the methodology analyzes the mutual location of the processing objects relative to each other, as well as relative to the classes from the catalog of reference values. The classification matrix is presented in the form of a set of dynamic lists. It allows reducing computational complexity while excluding matrix zero elements from processing and not allocating memory to store them. The functionality of processing classification results is extended. There is a possibility to fill in the catalog of reference values with the latest information. The decision support system, which implements the new methodology, allows the automated control system staff to assess the situation in detail and to adjust the automated control system work as the empirical knowledge is formed.