A wavelet-based feature set for recognizing pulse repetition interval modulation patterns

This paper presents a new feature set for the problem of recognizing pulse repetition interval (PRI) modulation patterns. The recognition is based upon the features extracted from the multiresolution decomposition of different types of PRI modulated sequences. Special emphasis is placed on the recognition of jittered and stagger type PRI sequences due to the fact that these types of PRI sequences appear predominantly in modern electronic warfare environments for some specific mission requirements and recognition of them is heavily based on histogram features. We test our method with a broad range of PRI modulation parameters. Simulation results show that the proposed feature set is highly robust and separates jittered, stagger, and other modulation patterns very well. Especially for the stagger type of PRI sequences, wavelet-based features outperform conventional histogram-based features. Advantages of the proposed feature set along with its robustness criteria are analyzed in detail.

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[1] Moore JB, Krishnamurthy V. Deinterleaving pulse trains using discrete-time stochastic dynamic-linear models. IEEE T Signal Proces 1994; 42: 3092-3103.
[2] Logothetis A, Krishnamurthy V. An interval-amplitude algorithm for deinterleaving stochastic pulse train sources. IEEE T Signal Proces 1998; 46: 1344-1350.
[3] Conroy TL, Moore JB. The limits of extended Kalman filtering for pulse train deinterleaving. IEEE T Signal Proces 1998; 46: 3326-3332.
[4] Orsi RJ, Moore JB, Mahony RE. Spectrum estimation of interleaved pulse trains. IEEE T Signal Proces 1999; 47: 1646-1653.
[5] Conroy TL, Moore JB. On the estimation of interleaved pulse train phases. IEEE T Signal Proces 2000; 48: 3420- 3425.
[6] Davies CL, Hollands P. Automatic processing for ESM. P IEEE Part F 1982; 129: 164-171.
[7] Mardia HK. New techniques for the deinterleaving of repetitive sequences. P IEEE Part F 1989; 136: 149-154.
[8] Milojevic DJ, Popovic BM. Improved algorithm for deinterleaving of radar pulses. P IEEE Part F 1992; 139: 98-104.
[9] Ray PS. A novel pulse TOA analysis technique for radar identification. IEEE T Aero Elec Sys 1998; 34: 716-721.
[10] Nishiguchi K, Kobayashi M. Improved algorithm for estimating pulse repetition intervals. IEEE T Aero Elec Sys 2000; 36: 407-421.
[11] Noone GP. A neural approach to automatic pulse repetition interval modulation recognition. In: Information, Decision and Control Conference; 810 February 1999; Adelaide, South Australia. New York, NY, USA: IEEE. pp. 213-218.
[12] Rong H, Jin W, Zhang C. Application of support vector machines to pulse repetition interval modulation recognition. In: 2006 6th International Conference on ITS Telecommunications; June 2006; Chengdu, China. New York, NY, USA: IEEE. pp. 1187-1190.
[13] Ryoo YJ, Song KH, Kim WW. Recognition of PRI modulation types of radar signals using the autocorrelation. IEICE T Commun 2007; 90: 1290-1294.
[14] Kauppi JP, Martikainen K, Ruotsalainen U. Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns. Neural Networks 2010; 23: 1226-1237.
[15] Hu G, Liu Y. An efficient method of pulse repetition interval modulation recognition. In: 2010 International Conference on Communications and Mobile Computing; 1214 April 2010; Shenzhen, China. New York, NY, USA:IEEE. pp. 287-291.
[16] Mahdavi A, Pezeshk AM. A robust method for PRI modulation recognition. In: 2010 IEEE 10th International Conference on Signal Processing; 2428 October 2010; Beijing, China. New York, NY, USA: IEEE. pp. 1873-1876.
[17] Song KH, Lee DW, Han JW, Park BK. Pulse repetition interval modulation recognition using symbolization. In: 2010 International Conference on Digital Image Computing: Techniques and Applications; 13 December 2010; Sydney, Australia. New York, NY, USA: IEEE. pp. 540-545.
[18] Keshavarzi M, Pezeshk AM, Farzaneh F. A new method for detection of complex pulse repetition interval modulations. In: 2012 IEEE 11th International Conference on Signal Processing; 2125 October 2012; Beijing, China. New York, NY, USA: IEEE. pp. 1705-1709.
[19] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Tran Pattern Anal 1989; 11: 674-693.
[20] Vetterli M, Kovacevic J. Wavelets and Subband Coding. Englewood Clifis, NJ, USA: Prentice Hall, 2007.
[21] Vapnik VN. An overview of statistical learning theory. IEEE T Neural Networ 1999; 10: 988-1000.
[22] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20: 273-297.
[23] M¨uller KR, Mika S, R¨atsch G, Tsuda K, Sch¨olkopf B. An introduction to kernel-based learning algorithms. IEEE T Neural Networ 2001; 12: 181-201.
[24] Daubechies I. Ten Lectures on Wavelets. Philadelphia, PA, USA: SIAM, 1992.