M.L. Artemov1, M.P. Slichenko2, S.P. Trusin3
1-3 JSC «Concern «Sozvezdie» (Voronezh, Russia)
1,3 Voronezh State University (Voronezh, Russia)
1 m.l.artemov@sozvezdie.su; 2 m.p.slichenko@sozvezdie.su
Formulation of the problem. The use of artificial neural networks to solve the problem of detecting and identifying signal structures as objects in the image is not oriented to work in a real, highly dynamic and saturated radio-electronic environment, characterized by intense fluctuations in radio signal parameters and changes in radio link parameters. The fundamental difficulty in creating an up-to-date, complete and reliable reference description of the radio technical parameters of detected radio links determines the need to create highly universal and adaptive algorithms for identifying radio links, which significantly limits the range of applications of the neural network approach to solving these problems.
Target. Develop and analyze a method for detecting the boundaries of a radio signal in the time-frequency domain.
Results. The paper proposes a new multidimensional matrix window operator and a corresponding statistical rule for adaptive detection of the corner of a rectangular signal region in a time-frequency panorama. An exact analytical expression is obtained for the distribution of the decisive detection statistics, the probabilities of false alarms and missed signals.
Practical significance. The implementation of the proposed approach makes it possible to detect and distinguish radio signals from modern radio communication and data transmission lines in a real complex electromagnetic environment, characterized by mutual overlap of frequency-time domains of signals from different radio lines with a priori uncertainty regarding the noise level.
Artemov M.L., Slichenko M.P., Trushin S.P. Method for detecting radio signal boundaries in the time-frequency domain based on a multidimensional matrix window operator. Radiotekhnika. 2024. V. 88. № 12. P. 5−15. DOI: https://doi.org/10.18127/j00338486-202412-01 (In Russian)
- Artjomov M. L., Borisov V.I., Makovij V.A., Slichenko M.P. Avtomatizirovannye sistemy upravlenija, radiosvjazi i radiojelektronnoj bor'by. Osnovy teorii i principy postroenija. Pod. red. M.L. Artjomova. M.: Radiotehnika. 2021 (in Russian).
- Artjomov M.L., Afanas'ev O.V., Slichenko M.P. Obnaruzhenie i pelengovanie istochnikov radioizluchenij v ramkah teorii statisticheskoj radiotehniki. Radiotehnika. 2016. T. 80. № 5. S. 4-18 (in Russian).
- Artjomov M.L., Afanas'ev O.V., Slichenko M.P. Metody statisticheskoj radiotehniki v sovremennom reshenii zadach radiomonitoringa. Antenny. 2016. № 6. S. 55-62 (in Russian).
- Artjomov M.L., Borisov S.G., Slichenko M.P. Harakteristiki maksimal'no pravdopodobnogo obnaruzhenija radiosignalov mono-impul'snymi obnaruzhiteljami-pelengatorami s antennoj sistemoj proizvol'noj konfiguracii. Radiotehnika, 2014. T. 78. № 11. S. 11-14 (in Russian).
- Artjomov M.L., Abramova E.L., Slichenko M.P. Prostranstvenno mnogokanal'noe adaptivnoe obnaruzhenie radiosignalov v chastotnoj oblasti pri neidentichnyh kanalah priema. Radiotehnika. 2014. T. 78. № 11. S. 5-10 (in Russian).
- Kostylev V.I., Slichenko M.P. Jenergeticheskoe obnaruzhenie radiosignalov na fone negaussovskogo shuma neizvestnoj intensivnosti. Izvestija vuzov. Ser. Radiofizika. 2009. T. 52. № 11. S. 910-920 (in Russian).
- Kostylev V.I., Slichenko M.P. Jenergeticheskoe obnaruzhenie chastichno poljarizovannyh radiosignalov na fone gaussovskogo shuma. Izvestija vuzov. Ser. Radiofizika. 2010. T. 53. № 12. S. 803-814 (in Russian).
- Kostylev V.I., Slichenko M.P. Reshajushhaja statistika jenergeticheskogo obnaruzhitelja pri prieme radiosignalov na fone poligaus-sovskogo shuma. Vestnik Voronezhskogo gosudarstvennogo universiteta. Serija «Fizika. Matematika». 2010. № 1. S. 26-30 (in Russian).
- Kostylev V.I., Slichenko M.P. Adaptivnoe jenergeticheskoe obnaruzhenie kvazideterminirovannyh radiosignalov na fone negaus-sovskogo shuma. Radiotehnika i jelektronika. 2011. T. 56. № 6. S. 698-704 (in Russian).
- Slichenko M.P. Mnogokanal'nyj jenergeticheskij obnaruzhitel' neizvestnyh kvazidetermi-nirovannyh radiosignalov. Teorija i tehnika radiosvjazi. 2014. № 3. S. 49-56 (in Russian).
- Sobel I., Feldman G. A 3´3 Isotropic Gradient Operator for Image Processing. 1968
- Prewitt J.M.S. (Lipkin B., Rosenfeld A., Eds.). Object enhancement and extraction. Picture Processing and Psychopictorics. New York: Academic Press. 1970. Р. 75-149.
- Roberts L.G. (Tippett J.T., et al., Eds.). Machine perception of three-dimensional solids. Optical and Electro-Optical Information Processing. May 1965
- Canny J. A Computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986. V. 8. № 6. Р. 679–698.
- Trajković M., Hedley M. Fast Corner Detection. Image and Vision Computing. 1998. V. 16. № 2. Р. 75-87.
- Přibyl B., Chalmers A., Zemcík P., Hooberman L. Evaluation of feature point detection in high dynamic range imagery. Journal of Visual Communication and Image Representation. 2016. V. 38. № 2. Р. 141-160.
- Smith S.M., Brady J.M. SUSAN-A new approach to low level image processing. International Journal of Computer Vision. 1997. V. 23. № 1. Р. 45-78.
- Rosten E. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. V. 32. № 1. Р. 105-119.
- Rattarangsi A., Chin R.T. Scale-based detection of corners of planar curves. Transactions on Pattern Analysis and Machine Intelli-gence. 1992. V. 14. Р. 430–434.
- Xiao Chen He, Nelson H. C. Yung. Corner detector based on global and local curvature properties. Optical Engineering. 2008. V. 47. № 5.
- Rodehorst V., Koschanb A. Comparison and evaluation of feature point detectors. 2006.
- Tuytelaars Tinne, Mikolajczyk Krystian. Local invariant feature detectors. A Survey Foundations and TrendsR in Computer Graphics and Vision. 2007. V. 3. № 3. Р. 177–280.
- Kester U. Analogovo-cifrovoe preobrazovanie. M.: Tehnosfera. 2007. 1019 s. (in Russian).
- Ajficher Je., Dzhervis B. Cifrovaja obrabotka signalov, prakticheskij podhod. M.: OOO «I.D. Vil'jams». 2004. 992 s. (in Russian).
- Vuds R., Gonsales R., Jeddins S. Cifrovaja obrabotka izobrazhenij v srede MatLab. M.: Tehnosfera. 2006. 616 s. (in Russian).
- Kolmogorov A.N. Osnovnye ponjatija teorii verojatnostej. M.: Fazis. 1998. 130 s. (in Russian).
- Slichenko M.P. Obobshhennye raspredelenija hi-kvadrat i Fishera-Sindekora v zadachah obanruzhenija istochnikov radio-izluchenija. Teorija i tehnika radiosvjazi. 2022. № 3. S. 45–51 (in Russian).
- Slichenko M.P. Mnogokanal'nyj jenergeticheskij obnaruzhitel' neizvestnyh kvazideterminirovannyh radiosignalov. Teorija i tehnika radiosvjazi. 2014. № 3. S. 49-56 (in Russian).
- Tihonov V.I. Optimal'nyj priem signalov. M.: Radio i svjaz'. 1983. 320 s. (in Russian).