Intra-Pulse Modulation Radar Signal Recognition Using I/Q Channels and Deep Learning
DOI:
https://doi.org/10.3849/aimt.02029Keywords:
intra-pulse modulation, recognition, deep learning, I/Q channels, Convolutional Neural Network (CNN), MATLAB simulationAbstract
This paper proposes a new method for classifying and recognizing intra-pulse modulation radar signals based on the I/Q channel and convolutional neural networks. The method is structured into two main stages. In the first stage, features of the received signal, such as linear frequency modulation, Costas and polyphase codes, are determined using the I/Q channel. A deep learning network is designed for signal recognition in the second stage. The proposed method is evaluated using simulated signals in a MATLAB environment. The results show that, compared to the existing methods, it reduces training time and provides a higher average accuracy compared with classic method, making it suitable for hardware implementation.
References
WILEY, R.G. ELINT: The Interception and Analysis of Radar Signals. London: Artech House, 2006. ISBN 1-58053-926-2.
PHILIP, P. Detecting and Classifying Low Probability of Intercept Radar. 2nd ed. London: Artech House, 2008. ISBN 1-59693-235-X.
DAVID, L.A. Introduction to Electronic Warfare Modelling and Simulation. London: Artech House, 2003. ISBN 978-1-58053-495-6.
HUBÁČEK, P. and J. VESELÝ. Základy teorie pasivních systémů IV (Funda-mentals of Passive Systems Theory IV) (in Czech). Brno: University of De-fence, 2010. ISBN 80-7231-758-X.
GIRON-SIERRA, J.M. Digital Signal Processing with Matlab Examples. Sing-pore: Springer, 2017. ISBN 978-981-10-2533-4.
HUBÁČEK, P., E. HOŠKO and J. VESELÝ. Základy teorie pasivních systémů I (Fundamentals of Passive Systems Theory I) (in Czech). Brno: University of Defence, 2007. ISBN 978-80-7231-476-8.
HUBÁČEK, P. and E. HOŠKO. Základy teorie pasivních systémů II (Funda-mentals of Passive Systems Theory II) (in Czech). Brno: University of Defence, 2008. ISBN 80-7231-614-1.
ZHIYU, Q., H. CHENFAN, H. CHANGBO and W. WENYANG. Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network and Deep Q-Learning Network. IEEE Access, 2020, 8, pp. 49125-49136. DOI10.1109/ACCESS.2020.2980363.
CAI, J., F. GAN, X. CAO, W. LIU and P. LI. Radar Intra–Pulse Signal Modula-tion Classification with Contrastive Learning. Remote Sensing, 2022, 14(22), 5728. DOI 10.3390/rs14225728.
ZHANG, X., J. ZHANG, T. LUO, T. HUANG, T. TANG and Z. CHEN. Radar Signal Intra-Pulse Modulation Recognition Based on a Denoising-Guided Dis-entangled Network. Remote Sensing, 2022, 14(5), 1252. DOI 10.3390/rs14051252.
CAI. J., Y. GUO and X. CAO. Automatic Radar Intra-Pulse Signal Modulation Classification Using the Supervised Contrastive Learning. Remote Sensing, 2024, 16(18), 3542. DOI 10.3390/rs16183542.
NADAV, L. and M. ELI. Radar Signals. Hoboken: Wiley, 2004. ISBN 0-471-47378-2.
CLARK, P. and D. CLARK. Introduction to Software Defined Radio. 2nd ed. Scotts Valley: CreateSpace, 2016. ISBN 1-5368-1476-8.
GOPI, E.S. Algorithm Collections for Digital Signal Processing Applications Using MATLAB. Dordrecht: Springer, 2007. ISBN 1-4020-6409-8.
VAN MINH, D., N. THI PHUONG and P. NHAT GIANG. SDR Implemented Algorithms for Real-time Intra-Pulse Modulated Radar Signal Analysis. In: 2025 10th IEEE International Conference on Integrated Circuits, Design, and Verification (ICDV). Ho Chi Minh City: IEEE, 2025. DOI 10.1109/ICDV66179.2025.11135420.
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