Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

References

[1] G. Lopez-Risueno, J. Grajal, A. Sanz-Osorio, “Digital channelized receiver based on Time-frequency analysis for signal interception,” IEEE Trans. Aerosp. Electron. Syst., vol. 41, pp. 879–898, 2005. doi: 10.1109/TAES.2005.1541437.Suche in Google Scholar

[2] D. S. Jang, H. L. Choi, J. E. Roh, “Optimization of surveillance beam parameters for phased array radars,”. In: IET International Conference on Radar Systems (Radar 2012), 2012, pp. 1–5.Suche in Google Scholar

[3] F. Gini, A. D. Maio, L. Patton, Waveform Design and Diversity or Advanced Radar Systems. Herts, U.K.: Institution of Engineering and Technology, 2011.10.1049/PBRA022ESuche in Google Scholar

[4] W. Melvin, J. Scheer, Principles of Modern Radar: Advanced Techniques. Herts, U.K.: SciTech Publishing, 2012.10.1049/SBRA020ESuche in Google Scholar

[5] M. Hurtado, T. Zhao, A. Nehorai, “Adaptive polarized waveform design for target tracking based on sequential Bayesian inference,” IEEE Trans. Signal Process., vol. 56, pp. 1120–1133, 2008. doi: 10.1109/TSP.2007.909044.Suche in Google Scholar

[6] R. G. Wiley, ELINT: The Interception and Analysis of Radar Signals. Boston: Artech House, 2006.Suche in Google Scholar

[7] K. Nishinuchi, M. Kobayashi, “Improved algorithm for estimating pulse repetition intervals,” IEEE Transactions Aerosp. Electron. Syst., vol. 56, pp. 407–421, 2000.10.1109/7.845217Suche in Google Scholar

[8] D. Zeng, X. Zeng, G. Lu, “Automatic modulation classification of radar signals using the generalised Time-frequency representation of Zhao, Atlas and marks,” IET Radar, Sonar Navigation, vol. 5, pp. 507–516, 2010.10.1049/iet-rsn.2010.0174Suche in Google Scholar

[9] W. L. Lai, T. Kind, H. Wiggenhauser, “Using ground penetrating radar and time–frequency analysis to characterize construction materials,” NDT_E Int., vol. 44, pp. 111–120, 2011. doi: 10.1016/j.ndteint.2010.10.002.Suche in Google Scholar

[10] I. Moraitakis, M. P. Fargues, “Feature extraction of intra-pulse modulated signals using Time-frequency analysis”. Proceedings of 21st Century Military Communications Conference, 2000, pp. 737–741.10.1109/MILCOM.2000.904028Suche in Google Scholar

[11] I. Ljubisa stankovic Djurovic, Srdjan Stankovic. “Instantaneous frequency in time–frequency analysis enhanced concepts and performance of estimation algorithms,” Digit. Signal Process., vol. 35, pp. 1–13, 2014.Suche in Google Scholar

[12] I. Orovic, M. Orlandic, S. Stankovic, Z. Uskokovic, “A Virtual Instrument for time-frequency analysis of signals with highly nonstationary instantaneous frequency,” IEEE Trans. Instrum. Meas., vol. 60, pp. 791–803, 2011. doi: 10.1109/TIM.2010.2060227.Suche in Google Scholar

[13] P. F. Pai, “Online tracking of instantaneous frequency and amplitude of dynamical system response,” Mech. Syst. Signal Process., vol. 24, pp. 1007–1024, 2010. doi: 10.1016/j.ymssp.2009.07.014.Suche in Google Scholar

[14] Y. Yang, Z. Peng, X. Dong, W. Zhang, G. Meng, “Application of parameterized time-frequency analysis on multicomponent frequency modulated signals,” IEEE Trans. Instrum. Meas., vol. 63, pp. 3169–3180, 2014. doi: 10.1109/TIM.2014.2313961.Suche in Google Scholar

[15] X. Xia, “A quantitative analysis of SNR in the short-time Fourier transform domain for multicomponent signals,” IEEE Trans. Signal Process., vol. 46, pp. 200–203, 1998. doi: 10.1109/78.651216.Suche in Google Scholar

[16] J. Pons Linares, J. A. Antonino-Daviu, M. Riera-Guasp. “Induction motor diagnosis based on a Transient Current analytic wavelet transform via frequency B-Splines,” IEEE Transactions Ind. Electron., vol. 58, pp. 530–1544, 2011.10.1109/TIE.2010.2081955Suche in Google Scholar

[17] V. Climente-Alarcon, J. A. Antonino Daviu, M. Riera-Guasp, R. Puche-Panadero, and L. Escobar, “Application of the Wigner–Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics,” Electr. Pow. Syst. Res., vol. 91, pp. 28–36, 2012.10.1016/j.epsr.2012.05.001Suche in Google Scholar

[18] R. Sharma, R. B. Pachori. “Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions,” Expert Syst. Appl., vol. 42, pp. 1106–1117, 2015. doi: 10.1016/j.eswa.2014.08.030.Suche in Google Scholar

[19] L. Angrisani, M. D’Arco, “A measurement method based on a modified version of the Chirplet transform for instantaneous frequency estimation,” IEEE Trans. Instrum. Meas., vol. 51, pp. 704–711, 2002. doi: 10.1109/TIM.2002.803295.Suche in Google Scholar

[20] J. Cui, W. Wong, “The adaptive Chirplet transform and visual evoked potentials,” IEEE Transactions Biomed. Eng., vol. 53, pp. 1378–1384, 2006. doi: 10.1109/TBME.2006.873700.Suche in Google Scholar PubMed

[21] B. Dugnol, C. Fernández, G. Galiano, J. Velasco, “On a Chirplet transform-based method applied to separating and counting wolf howls,” Signal Process., vol. 88, pp. 1817–1826, 2008. doi: 10.1016/j.sigpro.2008.01.018.Suche in Google Scholar

[22] Y. Yang, W. Zhang, Z. Peng, G. Meng, “Multicomponent signal analysis based on polynomial chirplet transform,” IEEE Trans. Ind. Electron., vol. 60, pp. 3948–3956, 2013. doi: 10.1109/TIE.2012.2206331.Suche in Google Scholar

[23] S. Peleg, B. Friedlander, “Multicomponent signal analysis using the polynomial-phase transform,” IEEE Trans. Aerosp. Electron. Syst., vol. 32, pp. 378–386, 1996. doi: 10.1109/7.481277.Suche in Google Scholar

[24] Y. Yang, Z. K. Peng, G. Meng, W. M. Zhang, “Spline-kernelled Chirplet transform for the analysis of signals with time-varying frequency and its application,” IEEE Trans. Ind. Electron., vol. 59, pp. 1612–1621, 2012. doi: 10.1109/TIE.2011.2163376.Suche in Google Scholar

[25] Y. Yang, Z. K. Peng, G. Meng, W. M. Zhang, “Characterize highly oscillating frequency modulation using generalized Warblet transform,” Mech. Syst. Signal Process., vol. 26, pp. 128–140, 2012. doi: 10.1016/j.ymssp.2011.06.020.Suche in Google Scholar

[26] J. Terrien, C. Marque, G. Germain, “Ridge extraction from the time-frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR,” IEEE Transactions Biomed. Eng., vol. 55, pp. 1496–1503, 2008. doi: 10.1109/TBME.2008.918556.Suche in Google Scholar PubMed

[27] L. J. Stankovic, I. Orovic, S. Stankovic, “Compressive sensing based separation of non-stationary and stationary signals over-lapping in time-frequency,” IEEE Transactions Signal Process., vol. 61, pp. 4562–4572, 2013,10.1109/TSP.2013.2271752Suche in Google Scholar

[28] S. Barbarossa, O. Lemoine, “Analysis of nonlinear FM signals by pattern recognition of their time-frequency representation,” IEEE Signal Process. Lett., vol. 3, pp. 112–115, 1996. doi: 10.1109/97.489064.Suche in Google Scholar

[29] A. Bouchikhi, A. Boudraa, J. Cexus, T. Chonavel, “Analysis of multicomponent LFM signals by teager Huang-Hough transform,” IEEE Trans. Aerosp. Electron. Syst., vol. 50, pp. 1222–1233, 2014. doi: 10.1109/TAES.2014.120202.Suche in Google Scholar

[30] I. Orović, S. Stanković, “Time-frequency-based speech regions characterization and eigenvalue decomposition applied to speech watermarking,” EURASIP J. Adv. Signal Process., article no. 4, pp. 572-574, 2010. doi: 10.1155/2010/572748.Suche in Google Scholar

[31] Z. Feng, F. Chu, M. J. Zuo, “Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation,” J. Sound Vibration, vol. 330, pp. 1225-1243, 2011. doi: 10.1016/j.jsv.2010.09.030.Suche in Google Scholar

[32] Y. Yang, X. Dong, Z. Peng, W. Zhang, G. Meng, “Component extraction for non-stationary multi-component signal using parameterized de-chirping and band-pass filter,” IEEE Signal Process. Lett., vol. 22, pp. 1373–1377, 2015. doi: 10.1109/LSP.2014.2377038.Suche in Google Scholar

[33] A. Francos, M. Porat, “Analysis and synthesis of multicomponent signals using positive Time-frequency distributions” IEEE Trans. Signal Process., vol. 47, pp. 493–514, 1999. doi: 10.1109/78.740132.Suche in Google Scholar

[34] L. Angrisani, M. D’Arco, D. Mauro, R. Schiano, Lo Moriello, M. Vadursi.“On the use of the warblet transform for instantaneous frequency estimation,” IEEE Trans. Instrum. Meas., vol. 54, pp. 1374–1380, 2005. doi: 10.1109/TIM.2005.851060.Suche in Google Scholar

[35] Y. Yang, X. J. Dong, Z. K. Peng, W. M. Zhang, G. Meng, “Vibration signal analysis using parameterized time–frequency method for features extraction of varying-speed rotary machinery,” J. Sound Vibration, vol. 335, pp. 350–366, 2015. doi: 10.1016/j.jsv.2014.09.025.Suche in Google Scholar

[36] H. K. Kwok, D. L. Jones, “Improved instantaneous frequency estimation using an adaptive short-Time Fourier transform,” IEEE Trans. Signal Process., vol. 48, pp. 2964–2972, 2000. doi: 10.1109/78.869059.Suche in Google Scholar

[37] S. Boyd, L. Vandeberghe, Convex Optimization. Press: Cambridge University, 2004.10.1017/CBO9780511804441Suche in Google Scholar

[38] H. Lee, Z. Bien, “A variable bandwidth filter for estimation of instantaneous frequency and reconstruction of signals with time-varying spectral content,” IEEE Trans. Signal Process., vol. 59, pp. 2052–2071, 2011. doi: 10.1109/TSP.2011.2113345.Suche in Google Scholar

[39] H. Kar, “A note on the improved LMI-based criterion for global asymptotic stability of 2-D state-space digital filters described by Roesser model using twoʼs complement overflow arithmetic,” Digit. Signal Process., vol. 23, pp. 1767–1772, 2013. doi: 10.1016/j.dsp.2013.06.006.Suche in Google Scholar

[40] S. Nakamori, “Design of RLS-FIR filter using covariance information in linear continuous-Time stochastic systems,” Appl. Math. Comput., vol. 219, pp. 9598–9608, 2013. doi: 10.1016/j.amc.2013.03.022.Suche in Google Scholar