The problem of the development of modern mobile smart-handled LPI radars using artificial intelligence technologies, the main difference of which is the construction of the CFAR function, which takes into account the influence of external and internal factors and requirements for the purpose, also distinguishes the developed radar among others in its class. The analysis of the publications was showed a great interest in modern radar systems and the lack of a unified approach to solving this problem. The purpose of the article is to reduce this gap, from collecting information from radar sensors and internal sensors to construct a generic multidimensional CFAR function and for organize its effect on the receiving and transmitting part of the radar. The application of artificial intelligence technologies in the construction of a modeling complex of LPI radars with CFAR function and their debugging in real time is covered.

The problem of developing the architecture of modern cognitive radar systems using artificial intelligence technologies is considered. The main difference from traditional systems is the use of a trained neural network. The heterogeneous multiprocessor system is rebuilt in the process of solving the problem, providing reliability and solving various types of problems of one class and deep learning of the neural network in real time. This architecture promotes the introduction of cognitive technologies that take into account the requirements for the purpose, the influence of external and internal factors.

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