Academic Journal
SELECTION AND RECOGNITION OF THE SPECIFIED RADIO EMISSIONS BASED ON THE AUTOREGRESSION SIGNAL MODEL
| Title: | SELECTION AND RECOGNITION OF THE SPECIFIED RADIO EMISSIONS BASED ON THE AUTOREGRESSION SIGNAL MODEL |
|---|---|
| Authors: | Bezruk, V. M., Kaliuznyi, N. М., Nikolaev, I. М., Qiang Guo, Zheng Yu |
| Source: | Radio Electronics, Computer Science, Control; № 2 (2020): Radio Electronics, Computer Science, Control; 7-14 Радиоэлектроника, информатика, управление; № 2 (2020): Радиоэлектроника, информатика, управление; 7-14 Радіоелектроніка, iнформатика, управління; № 2 (2020): Радіоелектроніка, інформатика, управління; 7-14 |
| Publisher Information: | Zaporizhzhia National Technical University, 2020. |
| Publication Year: | 2020 |
| Subject Terms: | Aвтоматизированный радиомониторинг, радиоизлучение, сигнал, авторегрессионная модель, селекция, распознавание, решающее правило, статистические испытания, система распознавания, 9. Industry and infrastructure, Aвтоматизований радіомоніторинг, радіовипромінювання, сигнал, авторегресійна модель, селекція, розпізнавання, правило прийняття рішення, статистичні випробування, система розпізнавання, recognition system, selection, autoregressive model, statistical tests, decisive rule, Automated radio monitoring, radio emission, autoregressive model, selection, recognition, decisive rule, statistical tests, recognition system, recognition, automated radio monitoring, radio emission |
| Description: | Context. A solution to the relevance problem of selecting and recognizing specified radio emissions in the presence of unknown radio emissions in automated radio monitoring is considered. It is proposed to solve the problem in an unconventional method for the recognition of statistically specified random signals in the presence of a class of unknown signals. Objective. The goal of the work is іnvestigation of the possibility of using random signal recognition methods in conditions of increased a priori uncertainty to solve the problem. The features of the signal recognition method are discussed, as well as the results of a study of the recognition quality indicators of given radio emissions, which are obtained by statistical modeling on samples of the corresponding signals. Method. The recognition method is based on the description of signals by a probabilistic model in the form of Gaussian autoregressive processes. It is proposed to use the new decision rule for the selection and recognition of statistically specified signals in the presence of unknown signals class. The proposed method of signal selection and recognition can be implemented in a recognition system that operates in training and recognition modes. In the training mode, unknown parameters of the decision rule are evaluated by classified samples of the given signals. Results. Research conducted by statistical tests on samples of the corresponding signals characteristic of automated radio monitoring of radio communications equipment. Practical results of studies of the problem of selection and recognition of specified radio emissions are presented. Values of indicators of quality of radio emissions recognition acceptable for the practice of radio monitoring are obtained. The dependences of quality indicators on some conditions and recognition parameters are investigated. Conclusions. Undertaken studies showed possibility of decision of problem by application of an unconventional method of selection and recognition of specified random signals. The practical significance lies in obtaining recommendations on the construction of systems for the recognition of radio emissions for specialists in the design of automated radio monitoring complexes. Such signal recognition systems are implemented by computer technology and is adaptive. The structure and parameters of the systems are set according to the samples of signals that are obtained for the corresponding given radio emissions. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 2313-688X 1607-3274 |
| DOI: | 10.15588/1607-3274-2020-2-1 |
| Access URL: | http://ric.zntu.edu.ua/article/download/207422/207534 http://ric.zntu.edu.ua/article/view/207422 http://ric.zntu.edu.ua/article/download/207422/207534 http://ric.zntu.edu.ua/article/view/207422 |
| Rights: | CC BY SA |
| Accession Number: | edsair.doi.dedup.....e6f9c1c0b14a0dba7d836c2293baf0df |
| Database: | OpenAIRE |
Be the first to leave a comment!