Akaike Bilgi Kriteri ile Radyo Frekans Geçici Hal Segment Tespiti

Yıl 2022, Cilt: 25 Sayı: 4, 1681 - 1686, 16.12.2022

Saleh Abulgasem Khalifa Ajouat , Necmi Serkan Tezel

https://doi.org/10.2339/politeknik.967341

Öz

RF verilerinin doğru olarak değerlendirilmesi, göndericinin açık olduğu zamanın tam olarak algılanmasından veya bilinmesinden başlar, bu zorluk iki önemli konuyu içerir; kaçınılmaz arka plan gürültüsü gibi gereksiz bilgileri işlemeyi önlemek ki bu işlemi hızlandır ve diğer konu, o gönderenin tam davranışını incelemektir. Bu çalışmada, Akaike Bilgi Kriterini (AIC) kullanılarak Bluetooth sinyalinin geçici olarak başlangıcını otomatik olarak yakalamak için bir yöntem geliştirilmiştir. Önerilen yöntem, en yaygın cep telefonu markalarından farklı yollarla alınan gerçek veriler üzerinde sinyal-gürültü oranının değişimi ile incelenmiştir. AIC algoritması, yüksek genlikli rastgele bir gürültüden etkilenmediğini göstermiştir.

Anahtar Kelimeler

Akaike bilgi kriteri , geçici durum algılama , bluetooth sinyali

Radio Frequency Transient Segment Detection Based on Akaike Information Criterion

Öz

The precise interpreting of RF data starts from retrieving or knowing the exact time instant at which moment the sender is turned on, this challenge implies two important issues; prevent manipulating redundant information such as unavoidable background noise which speed up the processing and the other issue is to study the exact behavior of that sender. A method has been developed to automatically catch the onset in transient of Bluetooth signal using of the Akaike Information Criterion (AIC). Present method has been examined on real world data taken from the most common cellular phones brands by different ways with variation of signal to noise ratio. The AIC algorithm shows robustness in the existence of relatively a high-amplitude random noise.

Anahtar Kelimeler

Akaike information criterion , transient detection , bluetooth signal

Kaynakça

• [1] Zhuo, Fei, Yuanling Huang, and Jian Chen. “Specific Emitter Identification based on the Energy Envelope of Transient Signal.” 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer. Atlantis Press, (2016).
• [2] J. Hall, M. Barbeau, and E. Kranakis, “Detection of transient in radio frequency fingerprinting using signal phase,” Wireless and Optical Communications. ACTA Press, (2003).
• [3] I. O. Kennedy, P. Scanlon, F. J. Mullany, M. M. Buddhikot, K. E. Nolan and T. W. Rondeau, “Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach,” IEEE 68th Vehicular Technology Conference, (2008).
• [4] O. Ureten and N. Serinken, “Wireless security through RF fingerprinting,” Canadian Journal of Electrical and Computer Engineering, Winter (2007).
• [5] Haataja, K. Hyppönen, K. Pasanen, S. Toivanen, P. “Bluetooth Security Attacks: Comparative Analysis, Attacks, and Countermeasures”, Springer, (2013).
• [6] S. Ur Rehman, K. Sowerby and C. Coghill, “RF fingerprint extraction from the energy envelope of an instantaneous transient signal,” Australian Communications Theory Workshop AusCTW, (2012).
• [7] Rahul G. Waghmare, Sanjay L. Nalbalwar, Arnab Das, “’Transient Signal Detection on the Basis of Energy and Zero Crossing Detectors”, Procedia Engineering’, 30, (2012).
• [8] Ureten, Oktay and Serinken, Nur., “Bayesian detection of radio transmitter turn-on transients”, (1999).
• [9] Y. Yuan, H. Wu, X. Wang, and Z. Huang, ‘‘Specific emitter identification based on Hilbert–Huang transform-based time–frequency–energy distribution features,’’ IET Comm,8, Sep. (2014).
• [10] I. S. Mohamed, Y. Dalveren and A. Kara, “Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method,” IEEE Access, 8, (2020).
• [11] Uzundurukan, E. & Dalveren, Y. & Kara, A. “A Database for the Radio Frequency Fingerprinting of Bluetooth Devices”, (2020).
• [12] Akaike, H, “Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory”, Akademiai Kiado, Budapest, (1973).
• [13] Sleeman, R and Van Eck T. "Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings.” Physics of the Earth and Planetary Interiors, (1999).
• [14] N. Maeda, “A method for reading and checking phase time in auto-processing system of seismic wave data,” Zisin (Journal of the Seismological Society of Japan. 2nd ser.), vol. 38, (1985).
• [15] D. Kim and H.-S. Oh, ‘‘EMD: A package for empirical mode decomposition and Hilbert spectrum,’’ R J., 1(1):(2009).