随着电磁对抗与反对抗技术的不断发展，低截获概率信号应用越来越广泛。传统的信号提取方法对低截获信号波形的提取已经无法满足用户使用需求。基于椭球基序列（discrete prolate spheroidal sequence，DPSS）构建时间窗函数对混合线性调频信号（multiple linear frequency modulation）进行分析，提出了恒门限判定概率捕获信号方法，并采用自适应二值化以及改进的霍夫变换（Hough transform）等图形处理算法完成信号波形的提取。仿真表明，本方法比短时傅里叶变换算法（short-time Fourier transform）具有更为优秀的噪声控制，同时在低信噪比下仍能准确提取波形。
With the continuous development of countermeasure and anti-countermeasure technology as well as the increasing application of low intercept signals, traditional signal extraction methods are no longer to satisfy user's needs for extraction of low intercept signal waveforms. In this paper, based on the discrete prolate spheroidal sequence (DPSS), the time-window function is used to analyze multiple linear frequency modulation signals. A method employing horizontal threshold decision probability to capture signal is proposed, and extraction of signal waveform is completed by adaptive binarization and improved Hough transform (HT). Simulation shows that the proposed method is better than the short-time Fourier transform algorithm in terms of noise control and can accurately extract the waveform at low SNR.
[1] Fuller K L. To see and not be seen[J]. Radar & Signal Processing Iee Proceedings F, 1990, 137(1):1-9.
[2] Pace P E. Detecting and classifying low probability of intercept radar[J]. Aeronautical Journal, 2003, 31(3):208-212.
[3] Azzouz E E, Nandi A K. Automatic modulation recognition of communication signals[M]. Springer US, NY, 1996.
[4] Nandi A K, Azzouz E E. Automatic analogue modulation recognition[J]. Signal Processing, 1995, 46(2):211-222.
[5] Cohen L. Time-frequency distributions:A review[J]. Proceedings of the IEEE, 1989, 77(7):941-981.
[6] Chen Z, Yi W, Blum R S, et al. Passive localization for emitter with unknown LFM signal based on signal parameter estimation[C]//2016 IEEE Radar Conference, Piscataway, NJ:IEEE, 2016:1-6.
[7] Chen H, Guo J H. The application of data-driven TF analysis methods in LFM signal parameter estimation[C]//TENCON 2013-2013 IEEE Region 10 Conference (31194). Piscataway, NJ:IEEE, 2013:1-4.
[8] Gao S L. Detection and parameters estimation of LFM signal based on chaos[J]. Journal of Sichuan University, 2013, 45(3):98-102.
[9] Wang Z Z, Huang Y, Liu F. Performance analysis of LFM signal parameter estimation through FrFT transform[J]. Applied Mechanics & Materials, 2012, 195/196:80-83.
[10] Percival D B, Walden A T. Spectral analysis for physical applications[M]. Cambridge:Cambridge University Press, 1993.
[11] Xu B, Sun L, Xu L, et al. Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip[J]. IEEE Transactions on Energy Conversion, 2013, 28(1):225-233.
[12] Slepian D. Prolate spheroidal wave functions, Fourier analysis, and uncertainty——V. The discrete case[J]. Bell Labs Technical Journal, 1978, 57(5):43-63.
[13] Thomson D J. Spectrum estimation and harmonic analysis[J]. Proceedings of the IEEE, 1982, 70(9):1055-1096.
[14] Gonzalez R C, Woods R E. Image processing[J]. Digital image processing, 2007, 2(7):731-733.
