A NEW ALGORITHM FOR AUTOMATIC DETECTION POLYPHASE-CODE RADAR SIGNALS UNDER THE NOISE FLOOR
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DOI:
https://doi.org/10.34238/tnu-jst.15473Abstract
In this paper, the author proposes a novel algorithm for detecting polyphase coded radar signals under the noise floor. The proposed algorithm consists of two main stages. In the first stage, the signals received are down-converted to an intermediate frequency with an instantaneous bandwidth of 200 MHz. In the second stage, a cross-correlation function and constant false alarm rate are employed to detect the signals and recover the baseband waveforms. The proposed algorithm is evaluated using commonly used polyphase-coded signals, including Barker, Frank, P, and Zadoff–Chu codes, through simulations conducted in MATLAB. The simulation results demonstrate that the proposed algorithm can detect polyphase coded radar signals at a signal-to-noise ratio of −11 dB, achieving a detection probability of 95% with a false alarm probability of 1e-5.It significantly outperforms traditional methods such as FFT (t = 50(s), SNR = 10 dB) and STFT (t = 85(s), SNR = 4 dB). Although the AI-based method (CNN) achieves a lower SNR threshold of −15 dB, it requires much longer processing time (t = 213(s)). Therefore, the proposed algorithm provides a practical and efficient solution for real-world radar reconnaissance applications that demand both high detection capability and fast processing speed.Downloads
References
L. Nadav and M. Eli, Radar Signals, 1st ed. Wiley-IEEE Press, New York, USA, 2004.
M. Skolnik, Introduction to Radar Systems, 3rd ed. McGraw-Hill Education, Ohio, USA, 2004.
L. V. Trees, K. L. Bell, and Z. Tian, Detection Estimation and Modulation Theory, Part I: Detection, Estimation and Filtering Theory, 2nd ed. Wiley-IEEE Press, New York, USA, 2001.
C. Leon, Time-Frequency Analysis, 1st ed. Prentice Hall, New Jersey, 1995.
S. Metuku and S. R. Karkarla, “Time-frequency Analysis of LPI Radar Signals Using Modified S-Transform,” International Journal of Electronic Engineering Research, vol. 9, no. 8, pp. 1267-1283, 2017.
X. Fan, B. Liu, D. Dong, and Y. Chen, “Blind Radar Signal Seperation Alogrithm Based on Third-Order Degree of Cyclostationary Criteria,” Journal of System Engineering and Electronics, vol. 35, no. 6, pp. 1441-1453, 2024, doi: 10.23919/JSEE.2024.000117.
A. Napolitano, Cyclostationary Processes and Time Series, 1st ed. Academic Press, Napoli, Italy, 2019.
T. K. Pal, “Artificial Intelligence based Radar Technology: Current Advancements, Future Directions and Challenges,” in 2025 IEEE Space, Aerospace and Defence Conference (SPACE), Bangalore, India, 2025, pp. 1-6, doi: 10.1109/SPACE65882.2025.11171388.
W. Feng, X. Hu, and X. He, “Artificial Intelligence (AI)-Based Radar Signal Processing and Radar Imaging,” Electronics, vol. 13, 2024, doi: 10.3390/electronics13214251.
L. Bangrui, “Advances in Radar Signal Processing: Integrating Deep Learning Approaches,” Highlights in Science, Engineering and Technology, vol. 97, pp. 40-45, 2024, doi: 10.54097/82nbts92.
K. Cheikh and S. Faozi, “Application of neural networks to radar signal detection in K-distributed clutter,” First International Symposium on Control, Communications and Signal Processing, Hammamet, Tunisia, 2004, pp. 295-298, doi: 10.1109/ISCCSP.2004.1296282.
C. Long, M. Xiaochuan, X. Qi, L. Bin, and R. Shiwei, “Performance Analysis of Some New CFAR Detectors under Clutter,” Journal of Computers, vol. 6, no. 6, pp. 1278-1285, 2011, doi: 10.4304/jcp.6.6.1278-1285.
X. Ni, H. Wang, Z. Xu, and C. Rong, “Polyphase Codes Radar Signal Recognition Based on STFT-SST and Deep Convolution Network,” Journal of Data acquisition and processing, vol. 35, no. 6, pp.1090-1096, 2020, doi: 0.16337/j.1004-9037.2020.06.008.
J. Liang, Z. Luo, and R. Liao, “Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network,” Sensors, vol. 24, 2024, doi: 10.3390/s24165344.
J. Cai, F. Gan, X. Cao, W. Liu, and P. Li, “Radar Intra–Pulse Signal Modulation Classification with Contrastive Learning,” Remote Sens., vol. 14, 2022, doi: 10.3390/rs14225728.
F. Ren, D. Quan, L. Shen, X. Wang, D. Zhang, and H. Liu, “LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning,” Electronics, vol. 12, no. 24, 2023, doi: 10.3390/electronics12244934.
