Methods for logical processing of images of radar objects marks based on semantic features
DOI:
https://doi.org/10.30837/rt.2024.1.216.12Keywords:
semantic analysis, radar signal, identification, extended atmospheric formations, aerial objectAbstract
This paper considers a method for logical processing of radar images based on semantic features. Algorithms and programs for automatic detection and recognition of radar aerial objects in surveillance radars with processing of real signals recordings are also given. The relevance of this work is the development of automatic information processing algorithm to ensure effective detection and recognition of radar object signals based on semantic features. The advantage of this method is the possibility to bring the image processing procedure of radar object images closer to the expert’s logic. It implies involvement in analysis of various distinguishing features between reflections from aerial objects. The problem of detecting and recognizing images of radar objects is transformed into the problem of feature classification. Therefore, the essence of logical image processing method based on semantic features is a making decision about detection and recognition of radar objects based on comparative analysis of features, which are defined on the set of semantic, fluctuation, geometric and energy components of radar image. An algorithm for automatic decision-making on detection and recognition of aircraft radar signals, including point-moving and stationary aircraft such as airplanes, helicopters, UAVs, is given. This approach of forming semantic features of radar signals, as well as the automation of information processing operations, increases the effectiveness of detecting weak signals due to accumulation of signal (energy) and logical information. At the same time, logical information is accumulated from the analysis of dynamic map of radar signal intensities with tracking of changes occurring in it during several radar soundings.
References
Li Jian 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 : СМИТ, 2007. 576 с.
Журавлев Ю. И. Об алгебраическом подходе к решению задач распознавания или классификации // Проблемы кибернетики. 2005. Вып. 33. С. 5–68.
Volodymyr Zhyrnov, Svitlana Solonska “Symbolic model of radar images when detecting aircraft”// Telecommunications and Radio Engineering. 2022. Vol. 81, Is. 2. P. 25–35.
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. Вып. 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. Вып. 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, Is. 17. Р. 1513–1519. DOI: 10.1615/TelecomRadEng.v79.i17.20.
Advanced Methods and Deep Learning in Computer Vision.1st Edition / Editors: E. R. Davies, Matthew Turk. Academic Press. 2021. Page Count: 586. ISBN: 9780128221099.
Downloads
Published
How to Cite
Issue
Section
License
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).