Intelligent model of radar object images for surveillance radars
Keywords:semantic analysis, radar signal, identification, aerial object
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.
Radar Signal Processing and Its Applications / Jian Li, R. Hummel, P. Stoica, E. G. Zelnio. Springer, 2013. 279 p.
Skolnik M. I. (eds) (2021) Radar Handbook. McGraw-Hill, New York.
Russel S. Artificial intelligence. A modern approach, Second Edition / S. Russel, P. Norvig. Williams, 2006. 1410 p.
Бондаренко М. Ф. Теория интеллекта : учебник / М. Ф. Бондаренко, Ю. П. Шабанов-Кушнаренко. Харьков : Изд-во СМИТ, 2007. 576 с.
Журавлев Ю. И. Об алгебраическом подходе к решению задач распознавания или классификации // Проблемы кибернетики. 2005. Вып. 33. С. 5 – 68.
Solonskaya S.V., Zhirnov V.V. Intelligent analysis of radar data based on fuzzy transforms // Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika). 2018. 77 (15). Рp. 1321 – 1329.
Жирнов В.В., Солонская С.В. Предикатная модель процессных знаний при обнаружении и распознавании пачечной структуры сигналов от летательных аппаратов в обзорных РЛС // Радиотехника. 2020. № 201. С. 137 – 144.
Jianping Ou, Jun Zhang and Ronghui Zhan. Processing Technology Based on Radar Signal Design and Classification // International Journal of Aerospace Engineering. Vol. 2020. Рр. 1 – 19. Article ID 4673763. https://doi.org/10.1155/2020/4673763.
Солонская С.В., Жирнов В.В. Предикатная модель процессных знаний при обнаружении и распознавании протяженных объектов типа облака, тучи, «ангел-эхо» в обзорных РЛС // Радиотехника. 2020. № 202. С 164 – 172.
Zhirnov V.V., Solonskaya S.V. Intelligent system for detection of low-visible air objects in surveillance radars // Telecommunications and Radio Engineering. 2020. Vol. 79, Iss. 17. P. 1513 – 1519. DOI: 10.1615/TelecomRadEng.v79.i17.20.
Advanced Methods and Deep Learning in Computer Vision.1st Edition / Ed.: E. R. Davies, Matthew Turk. Academic Press, 2021. Page Count: 586. ISBN: 9780128221099.
How to Cite
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).