Classification of aircraft targets with low-resolution radars based on multifractal spectrum features

References

• Shirman, YD, 2002. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. Boston, MA: Artech House; 2002.
   Google Scholar
• Ding, JJ, 2008. Target recognition techniques of surveillance radar. Beijing: National Defense Industry Press; 2008.
   Google Scholar
• Chen, F, Liu, HW, Du, L, and Bao, Z, 2010. Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra, Sci. China: Inform. Sci. 53 (2010), pp. 1446–1460.
   Web of Science ®Google Scholar
• Pouliguen, P, Lucas, L, Muller, F, Quete, S, and Terret, C, 2002. Calculation and analysis of electromagnetic scattering by helicopter rotating blades, IEEE Trans. Antennas Propagat. 50 (2002), pp. 1193–1408.
   Web of Science ®Google Scholar
• Bell, MR, and Grubbs, RA, 1993. JEM modeling and measurement for radar target identification, IEEE Trans. Aerospace Electron. Syst. 29 (1993), pp. 73–87.
   Web of Science ®Google Scholar
• Piazza, E, 1999. Radar signals analysis and modellization presence of JEM application in the civilian ATC radars, IEEE Aerospace Electron. Syst. Magazine. 14 (1999), pp. 35–40.
   Web of Science ®Google Scholar
• Martin, J, and Mulgrew, B, 1990. Analysis of the theoretical radar return signal from aircraft propeller blades, Record IEEE Int. Radar Conf. 1990 (1990), pp. 569–572.
   Google Scholar
• Huang JJ, Huang JX, Xie WX. Target classification by conventional radar. In: Proceedings of CIE International Conference of Radar; 1996. p. 204–207..
   Google Scholar
• Yang, SY, Yeh, SM, Bor, SS, Huang, SR, and Hwang, CC, 1997. Electromagnetic backscattering from aircraft propeller blades, IEEE Trans. Magn. 33 (1997), pp. 1432–1435.
   Web of Science ®Google Scholar
• Martin J, Mulgrew B. Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades. In: Proceedings of IET International Conference on Radar; 1992. p. 446–449..
   Google Scholar
• Elshafei, M, Akhtar, S, and Ahmed, MS, 2000. Parametric models for helicopter identification using ANN, IEEE Trans. Aerospace Electron. Syst. 36 (2000), pp. 1242–1252.
   Web of Science ®Google Scholar
• Ni, J, Zhang, SY, Miao, HF, and Zhang, B, 2011. Target classification of low-resolution radar based on fractional brown feature, Modern Radar. 33 (2011), pp. 98–101.
   Google Scholar
• Xian M, Zhuang ZW, Chen ZP, Guo GR. The fractal characteristic of radar target based on polarimetry. In: Proceedings of the IEEE 1996 National Aerospace and Electronics Conference; 1996, 1. p. 339–344..
   Google Scholar
• Mishra AK, Feng H, Mulgrew B. Fractal feature based radar signal classification. In: Proceedings of IET International Conference on Radar Systems; 2007. p. 1–4..
   Google Scholar
• Grassberger, P, 1983. Generalized dimensions of strange attractors, Phys. Lett. A. 97 (1983), pp. 227–230.
   Web of Science ®Google Scholar
• Halsey, TC, Jensen, MH, Kadanoff, LP, Procaccia, I, and Shraiman, BI, 1986. Fractal measures and their singularities: the characterization of strange sets, Phys. Rev. A. 33 (1986), pp. 1141–1151.
   PubMed Web of Science ®Google Scholar
• Hentschel, HGE, and Procaccia, I, 1983. The infinite number of generalized dimensions of fractals and strange attractors, Phys. D: Nonlinear Phenom. 8 (1983), pp. 435–444.
   Web of Science ®Google Scholar
• Telesca, L, Lapenna, V, and Macchiato, M, 2004. Mono- and multi- fractal investigation of scaling properties in temporal patterns of seismic sequences, Chaos, Solitons and Fractals 19 (2004), pp. 1–15.
   Web of Science ®Google Scholar
• Ding, JJ, and Zhang, XD, 2003. Studies of analysis of JEM signatures and classification of targets in the conventional radar, J. Electron. Inform. Technol. 25 (2003), pp. 956–962.
   Google Scholar
• Melendez GJ, Kesler SB. Spectrum estimation by neural networks and their use for target classification by radar. In: Proceedings of IEEE International Conference on Acoustics, Speech, and, Signal Processing; 1995. p. 3615–3618..
   Google Scholar
• Moses RL, Carl JW. Autoregressive modeling of radar data with application to target identification. In: Proceedings of the 1988 IEEE National Radar Conference; 1988. p. 220–224..
   Google Scholar
• Pellegrini SPF, Pardini CS. Radar signals analysis oriented to target characterization applied to civilian ATC radar. In: Proceedings of IET International Conference Radar; 1992. p. 438–445..
   Google Scholar
• Stove A. A Doppler-based target classifier using linear discriminants and principal components. In: Proceedings of IET Seminar on High Resolution Imaging and Target Classification; 2006. p. 171–176..
   Google Scholar
• Jahangir M, Pointing KM, O’Loghlen JW. A robust Doppler classification technique based on hidden Markov models. In: Proceedings of IEEE International Conference on Radar; 2002. p. 162–166..
   Google Scholar
• Jahangir MK, Pointing M, O’Loghlen JW. Correction to robust Doppler classification technique based on hidden Markov models. In: Proceedings of IEE International Conference on Radar, Sonar and Navigation; 2003. p. 150–387..
   Google Scholar
• Ji HB, Li J, Xie WX. Bispectrum based radar target classification. In: Proceedings of IEEE International Conference on Signal Processing; 1998. p. 419–422..
   Google Scholar
• Andric M, Durovic Z, Zrnic B. Ground surveillance radar target classification based on fuzzy logic approach. In: Proceedings of IEEE International Conference on Computer as a Tool; 2005. p. 1390–1392..
   Google Scholar
• Dullard, BD, and Dowdy, PC, 1991. Pulse Doppler signature of a rotary wing aircraft, IEEE Aerospace Electron. Syst. Magazine. 36 (1991), pp. 28–30.
   Google Scholar
• Yoon, S, Kim, B, and Kim, Y, 2000. Helicopter classification using time-frequency analysis, Electron. Lett. 36 (2000), pp. 1871–1872.
   Web of Science ®Google Scholar
• Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: John Wiley; 2001..
   Google Scholar