Recognition of electromagnetic sources with the use of deep neural networks

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 11055, Issue , 2019, Pages

  Matuszewski, Jan ^a   Pietrow, Dymitr ^b  

^a MILITARY UNIVERSITY OF TECHNOLOGY   (Poland)

^b Chojnice Niezychowice   (Poland)

Author keywords

electronic warfare; neural network; signal recognition

Indexed keywords

COMPUTER GRAPHICS; DEEP NEURAL NETWORKS; ELECTRONIC WARFARE; GRAPHICS PROCESSING UNIT; MILITARY APPLICATIONS; NEURAL NETWORKS; PROGRAM PROCESSORS; RADAR CROSS SECTION; RADIO NAVIGATION; SIGNAL GENERATORS;

ELECTROMAGNETIC SIGNALS; ELECTROMAGNETIC SOURCES; ELECTRONIC INTELLIGENCE; LEARNING COEFFICIENTS; RADIOLOCATION SYSTEMS; RECOGNITION SYSTEMS; SIGNAL RECOGNITION; SIMULATION ENVIRONMENT;

SIGNAL RECEIVERS;

EID: 85063905507     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2524536     Document Type: Conference Paper

References (30)

1. Ageyev, D., Bondarenko, O., Radivilova, T., Alfroukh, W., "Classification of existing virtualization methods used in telecommunication networks, " Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kiev, Ukraine, 24-27 May 2018, DOI: 10.1109/DESSERT.2018.8409104.
2. Carter, Ch.A.P., Masse, N., [Neural Networks for Classification of Radar Signals], Defence Research Establishment Ottawa, Technical Note 93-33, Nov. 1993, Ottawa, Canada.
3. Chen, S.;Wang, H.; Xu, F.; Jin, Y.Q., "Target Classification Using the Deep Convolutional Networks for SAR Images, " IEEE Transactions on Geoscience and Remote Sensing, 2016, 54, 4806-4817.
4. Chen, H., Zhang, F., Tang, B., Yin, Q., Sun, X., "Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition, " Remote Sensing, 2018, 10(10), 1618; DOI:10.3390/rs10101618.
5. C++ Multithreading: https://www.tutorialspoint.com/cplusplus/cpp-multithreading.htm (access 27.10.2018).
6. Dudczyk, J.; Kawalec, A., "Adaptive Forming the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network, " Int. J. Antennas Propagation, 2012, 935073.
7. Gregory, K., Miller, A., [C++ AMP Accelerated Massive Parallelism with Microsoft Visual C++], O'Reilly Media, Inc, 1005 Graven stein Highway North Sebastopol, California 95472.
8. Huang, Z.; Pan, Z.; Lei, B. "Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data, " Remote Sensing, 2017, 9, 907.
9. Learning to Compare Image Patches via Convolutional Neural Networks: http://www.cvfoundation. org/openaccess/content-cvpr-2015/papers/Zagoruyko-Learning-to-Compare-2015-CVPR-paper.pdf (access 28.05.2017).
10. Matuszewski, J., "Radar signal identification using a neural network and pattern recognition methods, " Proceedings of the 14th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science, TCSET 2018, Lviv-Slavske, Ukraine, 19-24 Feb. 2018, art. no. 7452040, pp. 79-83, DOI: 10.1109/TCSET.2018.8336160.
11. Matuszewski, J., Sikorska-Lukasiewicz, K., "Neural network application for emitter identification, " 18th International Radar Symposium (IRS), 28-30 June 2017, Prague, Czech Republic, 15 August 2017, Proceedings International Radar Symposium, art. no. 8008202, pp. 1-8, DOI: 10.23919/IRS.2017.8008202.
12. Michalewicz, Z., [Algorytmy genetyczne +struktury danych=programy ewolucyjne], Wyd. Naukowo-Techniczne, Warszawa 2003.
13. Microsoft Software Developer Network (MSDN), Visual Studio IDE User's Guide: https://msdn.microsoft.com/enus/library/jj620919(v=vs.120).aspx (access 19.10.2015).
14. Mussab Elamien Abd Elmaggeed, Abdelrasoul Jabar Alzubaidi, "Neural Network Algorithm for Radar Signal Recognition, " M Elamien Abd Elmaggeed Int. Journal of Engineering Research and Applications, Vol. 5, Issue 2, (Part 2), February 2015, pp.123-125.
15. Open Source Computer Vision (OpenCV): http://opencv.org/(dostp 28.05.2017).
16. Osowski, S.: [Sieci neuronowe do przetwarzania informacji]. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa, 2006.
17. Parrallel Neural Network Training with OpenCL: https://bib.irb.hr/datoteka/584308.MIPRO-2011-Nenad.pdf (access 27.10.2018).
18. PCLab.pl, 80 rdzeni Intela-szybko, oszczdnie i miniaturowo: https://pclab.pl/news24816.html (access 27.10.2018).
19. Petrov, N., Jordanov, I., "Radar Emitter Signals Recognition and Classification with Feedforward Networks, " 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems-KES2013, Procedia Computer Science, Vol. 22, 2013, pp. 1192-1200, DOI: 10.1016/j.procs.2013.09.206.
20. Pietkiewicz, T., Wajszczyk, B., "Fusion of identification information from ELINT-ESM sensors, " Proc. SPIE 10715, 2017 Radioelectronic Systems Conference, Jachranka, Poland, 19 April 2018, DOI: 10.1117/12.231648.
21. Pietrow, D., Matuszewski, J., "Object Detection and Recognition System Using Artificial Neural Networks and Drones, " 2017 Signal Processing Symposium, SPSSympo, Jachranka, 12-14.09.2017, Poland, art. no. 8053689, pp. 1-5, pp. 1-5, DOI: 10.1109/SPS.2017.8053689.
22. Rogers, S.K., Colombi, J.M., Martin, C.E., Gainey, J.C., "Neural networks for automatic target recognition, " Neural Networks, 1995, 8, 1153-1184.
23. Streaming SIMD Extensions: https://pl.wikipedia.org/wiki/Streaming-SIMD-Extensions (dostp 27.10.2018).
24. The Unreal Engine Academy: https://academy.unrealengine.com/(access 27.10.2018).
25. Tutorials for modern OpenGL (3.3+): http://www.opengl-tutorial.org (access 28.05.2017).
26. Vitalii, B., Kirichenko, L., Radivilova, T., "Classification of multifractal time series by decision tree methods, " 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. CEUR Workshop Proceedings 2105, Kiev; Ukraine, pp. 457-460.
27. Wong, M.D.; Nandi, A.K. "Automatic digital modulation recognition using artificial neural network and genetic algorithm, " Signal Processing, 2004, 84, pp. 351-365.
28. Wu, X., Shi, Y., Meng, W., Ma, X., Fang, N., "Specific emitter identification for satellite communication using probabilistic neural networks, " International Journal of Satellite Communications and Networking, 22 November 2018, DOI: 10.1002/sat.1286.
29. Yun; L., Jing-chao, L., "Radar signal recognition algorithms based on neural network and grey relation theory, " Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, 26-30 July 2011, Harbin, China, DOI: 10.1109/CSQRWC.2011.6037247.
30. Zhang, M., Diao, M., Gao, L., "Neural Networks for Radar Waveform Recognition, " Symmetry 2017, 9, 75, DOI: 10.3390/sym9050075.