Intelligent model of radar object images for surveillance radars

The results of developing an intelligent model of radar object images for surveillance radars are presented. The relevance of this work deals with the development of algorithm for automatic processing images of radar objects that provide effective detection of weak true signals due to the accumulation of signal and logical information in the analyzed cell and in its surroundings under interferences. The improvement of air safety tools and the automation of air traffic management processes require effective procedures to process signal information. The issues of more complete use and qualitative improvement of the information-processing capabilities of control systems are also topical, especially in difficult conditions of interfering signals. The basis of this study is the idea of using an intellectual model of radar object images for automatic decision-making on detection and recognition of radar objects, built on the space of semantic features. The main result is optical object recognition, similar to how an expert can easily recognize aerial objects and their types when viewing radar object images. Based on semantic features intelligent model of radar object images has been developed, which makes it possible to effectively detect and classify aerial objects. It is worth noting that the characteristic description of intelligent model of radar object images for point, extended, moving and stationary radar objects is the mathematical description of procedures and relationships at perception and analysis of signals in the form of distinguishing features or properties. As a result, various virtual images of radar object are generated in the form of spatial-semantic and spectral-semantic models. The main features and structural elements of the model are given. It is shown that the advantages of this model are related to the possibility of characteristic description of the radar object images using the algebra of finite predicates.