Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies

Introduction. The current need to obtain relevant, complete and reliable information about airborne objects has led to the continuous improvement of modern radar recognition systems (MRRS) as part of control systems. The development of modern MRRS has created objective prerequisites for the use of progressive and new methods and algorithms for the processing of signals using neural networks. The use of artificial neural networks with learning ability permits expansion to include many signs of recognition by using information obtained in the process of monitoring airspace.

Aim. To formulate the problem and develop proposals for the use of posterior information for airspace control in radar recognition systems using neural network technologies.

Materials and methods. Based on an analysis of the structure of a unified information network, an approach was formulated to facilitate the development of MRRS based on training technologies. Using the synthesis method, examples of technical solutions were proposed, which will allow the use of modern methods and signal processing algorithms using a posteriori information generated by the control system.

Results. The study identified the principles of neural network training in solving the recognition problem in the process of functioning of radio electronic equipment (REE). The technical solutions pro-posed take the functioning of the integrated radar system into account, allowing the information parameters required for training MRRS in a single information field to be obtained. It is shown that the removal of restrictions associated with the functional autonomy of REE, allows the use of posterior information in the implementation of radar recognition systems. This also allows for an increase in the number of recognition signs used in the algorithms and for the database of portraits to be replenished.

Conclusion. MRRS can be developed via training by removing the restrictions associated with the autonomous functioning of RES. This allows for the situational assessment to be enhanced and management decisions to be optimised.

1. Coban A. Y., Samotokin D. N. Scientific-Technical Problems of Development of the Federal System Exploration and Control of the Airspace of the Russian Federation and Ways to Solve Them. Military Thought. 2017, no. 4, pp. 14–19. (In Russ.)

2. Tait P. Introduction to Radar Target Recognition. London: Institution of Electrical Engineers. 2005 IET radar series, no. 18, 396 p.

3. Shkolnyi L. A., Tolstov E. F., Detkov A. N., Karpov O. A. Radar System of Aerial Reconnaissance, Interpretation of Radar Images. Moscow, VVIA n. a. N. E. Zhukovsky. 2008. 531 p. (In Russ.)

4. Shirman Ya. D, Gorshkov S. A., Leshchenko C. P., Bratchenko G. D., Orlenko V. M. Methods of Radar Recognition and their Modeling. Radar and Radiometry. 2000, no. 2, pp. 5–65. (In Russ.)

5. Tatuzov A. L. Neural Networks in Radar Problems. Moscow, Radio engineering, 2009, 432 p. (In Russ.)

6. Tolstikova E. B. Parametric Optimization of Nonlinear Neural Networks. Scientific notes of the Ukrainian research Institute of communications. 2014, no. 1, pp. 16–21. (In Russ.)

7. Haikin. S. Neural networks: full course. Moscow, Williams, 2006, 1104 p. (In Russ.)

8. Barsky A. B. Neural Networks. Recognition, Management, Decision-Making. Mosow, Finance and statistics, 2004, p. 176. (In Russ.)

9. Osovsky S. Neural Networks for Information Processing. Trans. from Polish by I. D. Rudinsky. Moscow, Finance and Statistics, 2002, 344 p. (In Russ.)

10. Tarkhov D. A. Neironnye seti. Modeli i algoritmy [Neural Networks. Models and Algorithms]. Мoscow, Radiotekhnika, 2005, 256 p. (In Russ.)

11. Abaturov V. A., Vasiliev O. V., Efimov V. A., Makaev V. E. Mathematical Models of Radar Signals Reflected from Air Targets of Different Classes. Radio engineering. 2006, no. 7, p. 28–33. (In Russ.)

12. Chistoprudov D. A., Kozlov V. A., Bibarsov M. R., Potyagov D. A., Karasik N. Ya. Construction and Training of Radial-Based Neural Networks for Receiving Telegraph and Code Structures. Journal of the Russian Universities. Radioelectronics. 2017, no. 6, pp. 28–35. (In Russ.)

13. Makaev V. E., Vasiliev O. V. Method of Radar Recognition of Air Targets by Turbine Effect. Radio engineering. 2000, no. 11, pp. 30–33. (In Russ.)

14. Zherdev D. A., Kazansky N. L., Fursov V. A. Recognition of Objects by Electromagnetic Scattering Diagrams of Electromagnetic Radiation Based on the Method of Reference Spaces. Computer Optics. 2014, no. 38, pp. 503–510. (In Russ.)

15. Mitrofanov D. G. Application of Neural Network Technology for Target Recognition by Radar Images. Neurocomputers: development and application, 2006, no. 3, pp. 60–68. (In Russ.)

16. Kostousov V. B., Kostousov A.V. Modeling of the Process of Guidance of Moving Objects by Radar Images. Gyroscopy and Navigation. 2004, vol. 2, pp. 37–47. (In Russ.)