تشخیص مدولاسیون درون پالسی با استفاده از اطلاعات زمان-فرکانسی مبتنی بر توزیع بهبودیافته B

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای جنگ الکترونیک، دانشگاه جامع امام حسین(ع)

2 دانشیار، دانشگاه صنعتی مالک اشتر

3 کارشناسی ارشد مخابرات، دانشگاه تبریز

چکیده

 در محیط جنگ الکترونیک، رادارها می­توانند دارای مدولاسیون‌های درون پالسی و بین پالسی متفاوتی باشند که باعث تمایز بین آنها      می­شود. تشخیص مدولاسیون درون پالسی در شرایطی که SNR منفی است موضوع مورد علاقه پژوهشگران است.  در این مقاله با استفاده از روش فرکانسی و زمان- فرکانس به تفکیک مدولاسیون‌های درون پالسی می­پردازیم. در این روش به تفکیک مدولاسیون‌های LFM، 4FSK، 2FSK، BPSK و NM می‌پردازیم. الگوریتم این روش بر مبنای ویژگی است و قادر به طبقه‌بندی تمام سیگنال­های راداری از این نوع مدولاسیون‌هاست. برای تشخیص مدولاسیون از ویژگی‌های زمان- فرکانسی مبتنی بر تبدیل زمان- فرکانس بهبودیافته B استفاده ‌شده است. نوآوری این مقاله نسبت به مقالات دیگر در استفاده از ویژگی‌های جدید از توزیع زمان فرکانس است. در این الگوریتم بعد از استفاده از توزیع زمان فرکانس، بعد آن کاهش داده‌شده است. و در هر فرکانس بیشترین مقدار زمانی در نظر گرفته‌شده و  ویژگی­های مدنظر از روی سیگنال استخراج ‌شده است. الگوریتم ارائه‌شده قابلیت تفکیک صددرصدی سیگنال­های راداری را برای این تعداد مدولاسیون درون پالسی تا نسبت سیگنال به نویز dB 11 را دارد. دوحالتی که روش‌های مشابه دقت کمتری در رنج dB 5- تا dB 5 دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Intra-pulse Modulation Recognition Using Time-Frequency Features Based on Modified-B Distribution

نویسندگان [English]

  • M. Sabetian 1
  • H. Dehghani 2
  • H. Ranaei 3
1 imam hossein university
2 malekashtar university
3 tabriz university
چکیده [English]

In the electronic warfare environment, radars can be differentiated according to intra-pulse and inter-pulse     modulations. Detection of intra-pulse modulation with negative SNR is a topic of interest to researchers. In this paper separation of intra-pulse modulation with frequency and time-frequency methods is presented. Using this method, we can categorize different types of LFM, 4FSK, 2FSK, BPSK, and NM modulations. The algorithm of this method is based on characteristics and it is able to classify all radar signals from these types of modulations. To detect the   modulation, time-frequency characteristics based on the improved time-frequency transform, B, have been used. The innovation in this research, is the use of new characteristics of time-frequency distribution. The proposed algorithm uses time-frequency distribution to analyze radar signals. Dimension reduction is performed next, then for each     frequency the maximum time value is considered and the characteristics are extracted from signal. The presented   algorithm has 100% capability of separating radar signals for this number of intra-pulse signals up to -11dB of SNR whereas similar methods have less accuracy with SNR range between -5db to 5db. 
 

کلیدواژه‌ها [English]

  • Intra-pulse modulation
  • Modified-B distribution
  • Probability of successful recognition
   [1]      W. Pei, QZ. Yang, Z. Jun, and T Bin, “Autonomous radar pulse modulation classification using modulation components analysis,” EURASIP J. Adv. Signal Process, pp. 1-11, 2016.##
   [2]      OA. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “Survey of automatic modulation classification techniques: classical approaches and new trends,” IET Com 1(2), pp. 137–156, 2007.##
   [3]      D. Grimaldi, S. Rapuano, and LD. Vito, “An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks,” IEEE Trans Instrum Meas vol. 56(5), pp. 1171–1720, 2007.##
   [4]      SZ. Hsue and SS. Soliman, “Automatic modulation classification using zero crossing, “IEEE Proc, Radar, Sonar Navig, vol. 137(6), pp. 459–464, 1990.##
   [5]      H. Alharbi, S. Mobien, S. Alshebeili, and F. Alturki, “Automatic modulation classification of digital modulations in presence of HF noise,” EURASIP J Adv Signal Process, vol. 33, pp. 3639–3654, 2012.##
   [6]      K. Hassan, I. Dayoub, W. Hamouda, and M. Berbineau, “Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems,” EURASIP J Adv Signal Process, vol. 1, pp. 1–13, 2010.##
   [7]      S. Qian and D. Chen, “Joint Time-Frequency Analysis,” IEEE Sig Process Mag, vol. 1, pp. 57-62, 1999.##
   [8]      F. Hlawatsch and GF. Boudreaux-Bartels, “Linear and quadratic time-frequency signal representations,” IEEE Sig Process Mag, vol. 2, pp. 325-332, 1992.##
   [9]      Z. Yang, W. Qiu, and H. Sun, “A Nallanathan, Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning Sensors,” vol. 1, pp. 1–14, 2016.##
[10]      G. Lopez-Risueno, J. Grajal, and A. Sanz-Osorio, “Digital Channelized Receiver Based on Time-Frequency Analysis for Signal Interception,” IEEE Trans Aerosp Electron System, vol. 3, pp. 879–898, 2005.##
[11]      Y. Zhang, X. Ma, and D. Cao, “Automatic Modulation Recognition Based on Morphological Operations,” Circuits System Signal Process vol. 5, pp. 2517–2515, 2013.##
[12]      X. Ma, Dan. Liu, and Y. Shan, “Intra-pulse modulation recognition using short-time ramanujan fourier transform spectrogram,” EURASIP Journal on Advances in Signal Processing, vol. 3, 2017.##
[13]      D. Zeng, X. Zeng, H. Cheng, and B. Tang, “Automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform,” IET Radar Sonar Navig vol. 5, pp 322-331, 2012.##
[14]      TJ. Lynn and AZ. Shamer, “Automatic analysis and classification of digital modulation signals using spectrogram time frequency analysis,” Proc.  International Symposium on Communications & Information Technologies, Sydney, pp. 916-920, 2007.##
[15]      F. Xie, C. Li, and G. Wan, “An Efficient and Simple Method of MPSK Modulation Classification,” 4th International Conf on Wireless Communications, Networking and Mobile Computing ,WiCOM 08, Dilian, China, pp. 1–3, 2008.##
[16]      J. Lerga, V. Sucic, and B. Boashash, “An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR,”  EURASIP Journal on Advances in Signal Processing, vol. 5, 16 pages, 2011.##
[17]      A. H. Davaie Markazi and M. Nazarahari, “Application of DWT for Ship’s Acoustic Signal Identification Using Feature Extraction Methods and Ensemble Learning,”Modarres (In Persian).##
[18]      K. Martinm aki, H. Rusko, S. Saalasti, and J. Kettunen, “Ability of short-time Fourier transform method to detect transient changes invagal effects on hearts: a pharmacological blocking study,” Am. J. Physiol. Heart Circ. Physiol., vol. 290, no.6, pp. 2582-2589, 2006.##
[19]      A. S. Keselbrener, “Selective discrete Fourier transform algorithm for time- frequency analysis: method and application on simulated and cardiovascular signals,” IEEE Trans. Biomed. Eng., vol. 43, no. 8, pp. 789-802, 1996.##
[20]      P. N. V. Novak, “Time/frequency mapping of the heart rate, blood pressure and respiratory signals,” Med. Biol. Eng. Comput, vol. 31, no.2, pp. 103-110, 1993.##
[21]      S. MAEM and M. C. Pola, “Estimation of the power spectral density in non-stationary cardiovascular time series: assessing the role of the time-frequency representations,” IEEE Trans. Biomed. Eng., vol. 43, no. 1, pp. 46-59, 1996.##
[22]      S. B. Jasson, “Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-/frequency-domain method,” Circulation, vol. 96, no. 10, pp.            3521-3526, 1991.##
[23]      A. Bianchi, L. Mainardi, C. Meloni, S. Chierchia, and S. Cerutti, “Continuous monitoring of the sympatho-vagal balance through spectral analysis .Recursive autoregressive techniques for tracking transient events in heart rate signals,” IEEE Eng. Med. Biol. Mag., vol. 16, no.  5, pp. 64-73, 1997##.
[24]      O. Meste, B. Khaddoumi, G. Blain, and S. Bermon,      “Time-varying analysis methods and models for the respiratory and cardiac system coupling in graded exercise,” IEEE Trans. Biomed. Eng. vol. 52, no. 11, pp. 1921-1930, 2005.##
[25]      B. Boashash, ed., “Time Frequency Signal Analysis and Processing,” Acomprehensive Reference, Elsevier, The Boulevard,Langford Lane, Kidlington, Oxford, UK, 2003.##
[26]      H. I. Choi and W. J. Williams, “Improved time-frequency representation of multicomponent signals using exponential kernels,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 6, pp. 862–871, 1989.##
[27]      M. G. Amin and W. J. Williams, “High spectral resolution time-frequency distribution kernels,” IEEE Transactions on Signal Processing, vol. 46, no. 10, pp. 2796–2804, 1998.##
[28]      L. Stankovic, “On the realization of the polynomial wignerville distribution for multicomponent signals,” IEEE Signal Processing Letters, vol. 5, no. 7, pp. 157–159, 1998.##
[29]      Z. M. Hussain and B. Boashash, “Adaptive instantaneous frequency estimation of multicomponent FM signals using quadratic time-frequency distributions,” IEEE Transactions on Signal Processing, vol. 50, no. 8, pp. 1866–1876, 2002.##
[30]      L. Rankine, M. Mesbah, and B. Boashash, “IF estimation for multicomponent signals using image processing techniques in the time-frequency domain,” Signal Processing, vol. 87, no. 6, pp. 1234–1250, 2007.##
[31]      Z. Zarei, M. M. Madani, and R. Mohseni, “Detection of Phase Code Modulated LPI Radar Signals using               Time-Frequency Distributions and Comparing with Power Function of Matched Detector,” Journal of Radar, vol. 2, no. 4, 2015 (in persian).##