Vol. 44
Latest Volume
All Volumes
PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2013-09-21
Specific Emitter Identification Based on Transient Energy Trajectory
By
Progress In Electromagnetics Research C, Vol. 44, 67-82, 2013
Abstract
Specific emitter identification (SEI) is the technique which identifies the individual emitter based on the RF fingerprint of signal. Most existing SEI techniques based on the transient RF fingerprint are sensitive to noise and need different variables for transient detection and RF fingerprint extraction. This paper proposes a novel SEI technique for the common digital modulation signals, which is robust to Gaussian noise and can avoid the problem that different variables are needed for transient detection and RF fingerprint extraction. This makes the technique more practical. The technique works based on the signal's energy trajectory acquired by the fourth order cumulants. A relative smoothness measure detector is used to detect the starting point and endpoint of the transient signal. The polynomial fitting coefficients of the energy trajectory and transient duration form the RF fingerprint. The principal component analysis (PCA) technique is used to reduce the feature vector's dimension, and a support vector machine (SVM) classifier is used for classification. The signals captured from eight mobile phones are used to test the performance of the technique, and the experimental results demonstrate that it has good performance even at low SNR levels.
Citation
Ying-Jun Yuan, Zhitao Huang, and Zhi-Chao Sha, "Specific Emitter Identification Based on Transient Energy Trajectory," Progress In Electromagnetics Research C, Vol. 44, 67-82, 2013.
doi:10.2528/PIERC13080703
References

1. Danev, B., H. Luecken, S. Capkun, and K. EI Defrawy, "Attacks on physical-layer identification," Proc. ACM Conf. on Wireless Network Security,, 89-98, 2010.

2. Shaw, D. and W. Kinsner, "Multifractal modeling of radio transmitter transients for classification," Proc. WESCANEX'97, 306-312, 1997.

3. Ureten, O. and N. Serinken, "Detection of radio transmitter turnon transients," Electronics Letters, Vol. 35, No. 23, 1996-1997, 1999.
doi:10.1049/el:19991369

4. Ureten, O. and N. Serinken, "Bayesian detection of Wi-Fi transmitter RF fingerprints," Electronics Letters, Vol. 41, No. 6, 373-374, 2005.
doi:10.1049/el:20057769

5. Hall, J., M. Barbeau, and E. Kranakis, "Detection of transient in radio frequency fingerprinting using signal phase," Proceedings of the 3rd IASTED Int. Conf. on Wireless and Optical Communications, 13-18, 2003.

6. Lee, T. W., "IndependComponent Analysis: Theory and Applications," Kluwer Academic Publishers, 1999.

7. Donelli, M., "A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm," Progress In Electromagnetics Research M, Vol. 19, 173-181, 2011.
doi:10.2528/PIERM11061206

8. Hall, J., M. Barbeau, and E. Kranakis, "Enhancing intrusion detection in wireless networks using radio frequency fingerprinting," Proceedings of the 3rd IASTED International Conference on Communications, Internet and Information Technology (CIIT), 201-206, 2004.

9. Hall, J., M. Barbeau, and E. Kranakis, "Detecting rogue devices in bluetooth networks using radio frequency fingerprinting," IASTED International Conference on Communications and Computer Networks , 108-113, 2006.

10. Ur Rehman, S., K. Sowerby, and C. Coghill, "RF fingerprint extraction from the energy envelope of an instantaneous transient signal," Australian Communications Theory Workshop (AusCTW), 90-95, 2012.
doi:10.1109/AusCTW.2012.6164912

11. Bonne Rasmussen, K. and S. Capkun "Implications of radio ¯ngerprinting on the security of sensor networks," Proceedings of the Third International Conference on Security and Privacy in Communications Networks and the Workshops, IEEE,, 331-340, 2007.

12. Afolabi, O., K. Kim, and A. Ahmad, "On secure spectrum sensing in cognitive radio networks using emitters electromagnetic signature," Proceedings of 18th Internatonal Conference on Computer Communications and Networks , 1-5, 2009.

13. Ellis, K. and N. Serinken, "Characteristics of radio transmitter fingerprints," Radio Science, Vol. 36, No. 4, 585-597, 2001.
doi:10.1029/2000RS002345

14. Xu, J., H. Zhao and T. Liang, "Method of empirical mode decompositions in radio frequency fingerprint," 2010 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 1275-1278, 2010.

15. Zhao, C., L. Huang, L. Hu, and Y. Yao, "Transient fingerprint feature extraction for WLAN cards based on polynomial fitting," The 6th International Conference on Computer Science & Education (ICCSE 2011), 1099-1102, 2011.
doi:10.1109/ICCSE.2011.6028826

16. Klein, R. W., M. A. Temple, and M. J. Mendenhall, "Application of wavelet-based RF fingerprinting to enhance wireless network security," Journal of Communications and Networks, 544-555, 2009.
doi:10.1109/JCN.2009.6388408

17. Wang, L. and Y. Ren, "Recognition of digital modulation signals based on high order cumulants and support vector machines," ISECS International Colloquium on Computing, Communication, Control, and Management, 271-274, 2009.
doi:10.1109/CCCM.2009.5267733

18. Zhou, X., Y.Wu, and B. Yang, "Signal classification method based on support vector machine and high-order cumulants," Wireless Sensor Network, 48-52, 2010.

12. Swami, A. and B. M. Sadler, "Hierarchical digital modulation classification using cumulants," IEEE Transactions on Communications, Vol. 48, No. 3, 416-429, 2000.
doi:10.1109/26.837045

20. Paige, C. and LSQR: An algorithm for sparse linear, "LSQR: An algorithm for sparse linear equations and sparse least squares," ACM Trans. Math. Software, Vol. 8, 43, 1982.
doi:10.1145/355984.355989

21. Robert-Granie, C., J.-L. Foulley, E. Maza, and R. Rupp, "Statistical analysis of somatic cell scores via mixed model methodology for longitudinal data," Anim. Res, Vol. 53, 259-273, 2004.
doi:10.1051/animres:2004016

22. Xu, S., B. Huang, L. Xu, and Z. Xu, "Radio transmitter classi¯cation using a new method of stray features analysis," combined with PCA Military Communications Conference (MILCOM 2007), 1-5, 2007.
doi:10.1109/MILCOM.2007.4454838

23. Burges, C. J. C., "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, 121-167, 1998.
doi:10.1023/A:1009715923555

24. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801

25. Bermani, E., A. Boni, S. Caorsi, M. Donelli, and A. Massa, "A multi-source strategy based on a learning-by-examples technique for buried object detection," Progress In Electromagnetics Research, Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701

26. Tekbas, O. H., O. Ureten, and N. Serinken, "Improvement of transmitter identification system for low SNR transients," Electronics Letters, Vol. 40, No. 3, 192-183, 2004.
doi:10.1049/el:20040160