Elektronik destek sistemleri için frekans kestirim yöntemlerinin performans analizi

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Gamze Cabadağ , Ömer Karal

https://doi.org/10.2339/politeknik.1262997

Öz

Elektronik harp sistemlerinde, tehdit radarının frekansının doğru olarak kestirimi radarın kimliklendirilmesi ve elektronik atak uygulanabilmesi için oldukça önemlidir. Hızlı Fourier dönüşümü (FFT) ve Sayısal anlık frekans kestirimi (DIFM) en yaygın kullanılan frekans kestirim yöntemleridir. Tehdit radarının ara frekans (IF) değeri FFT bin’lerinin tam katı değilse FFT yöntemi ile frekans kestirimi istenilen doğrulukta elde edilemez. Doğruluğu artırmak için genellikle FFT’nin çıkışına interpolasyon teknikleri uygulanır. DIFM yönteminde ise, sayısallaştırılmış I/Q sinyali belirli bir süre geciktirilip eşleniği alınır ve eşlenik ile orijinal I/Q sinyali çarpılarak faz hesaplanır. Bu çalışmada, Jacobsen ve iyileştirilmiş Quinn interpolasyon teknikleri uygulanmış FFT yöntemi ile uygun gecikme süresi hesaplanmış DIFM yönteminin frekans kestirim performansları, işaret gürültü oranı (SNR) değiştirilerek Gauss gürültüsü altında kapsamlı bir şekilde analiz edilmiştir. Ayrıca, gerçek zamanlı sistemler için frekans kestiriminin hızlı olması oldukça önemli olduğu için FFT, FFT ve Jacobsen, FFT ve iyileştirilmiş Quinn ve DIFM frekans kestirim yöntemleri hesaplama zamanı açısından da karşılaştırılmıştır. Her bir yöntem için 100 adet Monte Carlo denemesi uygulanmış ve frekans kestirimindeki hata, ortalama hata kare kökü (RMSE) cinsinden sunulmuştur. Matlab ortamında gerçekleştirilen simülasyonların sonuçlarına göre FFT ve iyileştirilmiş Quinn yönteminin FFT ve Jacobsen yöntemine göre genellikle daha iyi frekans kestirimi yaptığı gözlemlenmiştir. Ayrıca, SNR seviyesi arttıkça, FFT, FFT ve iyileştirilmiş Quinn ve FFT ve Jacobsen yöntemlerine göre DIFM yönteminin daha iyi performansa (düşük RMSE değerine) sahip olduğu gözlemlenmiştir.

Anahtar Kelimeler

Frekans kestirimi, İnterpolasyon yöntemleri, Jacobsen, Quinn, FFT, DIFM

Performance Analysis of frequency estimation methods for electronic support systems

Öz

In electronic warfare systems, accurate estimation of the threat radar frequency is so important for radar identification and electronic attack. Digital instantaneous frequency estimation (DIFM) and fast Fourier transform (FFT) are the most widely used frequency estimation methods. If the intermediate frequency (IF) value of the threat radar is not an exact multiple of the FFT bins, frequency estimation with the FFT method cannot be obtained with the desired accuracy. Interpolation techniques are often applied to the output of the FFT to improve accuracy. In the DIFM method, the digitized IF signal is delayed for a certain time and its conjugate is taken and phase is calculated by multiplying the conjugate with the original I/Q signal. In this study, the frequency estimation performances of the FFT technique that Jacobsen and Improved Quinn interpolation techniques were applied and DIFM method with appropriate delay time extensively analyzed by changing the signal to noise ratio (SNR) under Gauss noise. Also, since fast frequency estimation is so important for real time systems, FFT, FFT and Jacobsen, FFT and improved Quinn and DIFM frequency estimation methods were also compared in terms of computation time. For each method, one hundred Monte Carlo trials were applied and the error in the frequency estimation is presented in terms of the root mean square error (RMSE). According to the simulation results performed in the MATLAB environment, it has been observed that the FFT and the improved Quinn method generally provide better frequency estimation than the FFT and Jacobsen method. In addition, as the SNR level increased, it was observed that the DIFM method had better performance (lower RMSE value) than the FFT, FFT and improved Quinn and FFT and Jacobsen methods.

Anahtar Kelimeler

Frequency estimation, interpolation methods, Jacobsen, Quinn, FFT, DIFM

Kaynakça

• [1] Arık, D. T., Karal, Ö., & Şahin, A. B., “A Comparative Study of Artificial Neural Networks and Naïve Bayes Techniques for the Classification of Radar Targets”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(4), 1779-1788, (2020).
• [2] Cabadağ, G., & Karal, Ö., “Analysis of Prony’s and Pisarenko Frequency Estimation Methods at Different Bandwidths, Different Noise And Variances”, 30th Signal Processing and Communications Applications Conference (SIU) pp. 1-4, (2022).
• [3] Muslu, E.A., Karal, Ö., “Mathematical Modeling of Threats in Electronic Warfare Systems”, 29th Signal Processing and Communications Applications Conference (SIU). p. 1-4.(2021).
• [4] Yildinm, S. A., Orduyılmaz, A., Serin, M., & Yildmm, A. “Multitab digital instantaneous frequency measurement receiver”, 25th Signal Processing and Communications Applications Conference (SIU), p.1-4, (2017).
• [5] Arık, D.T; Şahin,A.B.,” Target classification with FMCW radar using features extracted from Fourier transform of radar cross section”, 27th Signal Processing and Communications Applications Conference (SIU) p. 1-4, (2017).
• [6] İleri K. Tezel, N. S. “The Impact of Channel Errors in Passive Coherent Location Radar using FM Base Stations”, Politeknik Dergisi, 25(2), 503-511, (2020).
• [7] Tsui, J.B. “Special design topics in digital wideband receivers”, Artech House, p.21, (2010).
• [8] Wiley, R. G. , “ELINT The Interception and Analysis of Radar Signals”, Artech House Radar Library, (2006).
• [9] Gençol, K., “A two-stage deinterleaving technique for clustering of radar pulses”, 25th Signal Processing and Communications Applications Conference (SIU) , pp. 1-4, (2017).
• [10] Center, Naval Air Warfare. “Electronic warfare and radar Systems engineering hangbook.”, Electronic Warfare Division, Pont Mugu, CA, (1997).
• [11] Tuncer, E., Bolat, E. D, “Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması”, Politeknik Dergisi, 1-1.(2021).
• [12] Ahmed, M. Y., Keskin, İ., “A simulation on soil structure interaction with ABAQUS; effect on the behavior of a concrete building of soil layers and earthquake properties”, Politeknik Dergisi, 1-1, (2023).
• [13] Ortatatlı, İE., “ Elektronik destek sistemlerinde gerçek zamanlı tehdit radar parametreleri çıkarımı”, Master's Thesis TOBB Ekonomi ve Teknoloji Üniversitesi, (2017).
• [14] Herselman, P. L., & Cilliers, J. E. A., “Digital instantaneous frequency measurement technique using high-speed analogue-to-digital converters and field programmable gate arrays: the csir at 60”, South African Journal of Science, 102(7): 345-348, (2006).
• [15] Darvin, R. C., Paranjape, H., Mohanan, S. K., Elango, V., “Analysis of autocorrelation based frequency measurement algorithm for IFM receivers”, IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 1-6, (2014).
• [16] Niranjan, R. K., & Naik, B. R., “FPGA based implementation of pulse parameters measurement”, IEEE Science and Information Conference, 862-867, (2014).
• [17] Kvachev, M. A., Puzyrev, P. I., & Semenov, K. V., “Research of Instantaneous Frequency Measurement Receiver”, IEEE Dynamics of Systems, Mechanisms and Machines (Dynamics) Conference, 1-5, (2020).
• [18] Sree, A.R., Rao, T.V., “Sensivitity Enhancement in Digital Instantaneous Frequency Measurement“, International Journal of Innovative Research in Science, Engineering and Technology, 3.9, (2014).
• [19] Gasior, M., & Gonzalez, J. L. “Improving FFT frequency measurement resolution by parabolic and Gaussian spectrum interpolation”, AIP Conference Proceedings, Vol. 732, No. 1, pp. 276-285, (2004).
• [20] Ligges U., “Transkription monophoner Gesangszeitreihen”, Ph.D. Thesis, Faculty of Statistics, TU of Dortmund, Germany, (2006).
• [21] Bischl, B., Ligges, U., & Weihs, C., “Frequency estimation by DFT interpolation: A comparison of methods”, Technical Report, (2009).
• [22] Iglesias, V., Grajal, J., Yeste-Ojeda, O., Garrido, M., Sánchez, M. A., López-Vallejo, M., “Real-time radar pulse parameter extractor”, IEEE Radar Conference 0371-0375, (2014).
• [23] Minda, A. A., Lupu, D., Gillich, G. R. “Improvement of Jain’s algorithm for frequency estimation”. Studia Universitatis Babes-Bolyai Engineering, 65(1), (2020).
• [24] Minda, A. A., Barbinitia, C. I., & Gillich, G. R. A. “ A review of Interpolation Methods Used for Frequency Estimation” Romanian Journal of Acoustics and Vibration, 17(1), 21-26, (2020).
• [25] Quinn, B. G., “Estimating frequency by interpolation using Fourier coefficients”, IEEE transactions on Signal Processing, 42(5): 1264-1268, (1994).
• [26] Koç, M., “Bazı Ayrık Fourier Dönüşümüne Dayalı Frekans Kestiricilerin Karşılaştırmalı Performans Analizi”, Yüksek lisans tezi, Uludağ Üniversitesi, Bursa, (2021).
• [27] Candan, Ç. “A method for fine resolution frequency estimation from three DFT samples”, IEEE Signal processing letters, 18(6), 351-354, (2011).
• [28] Candan, Ç. “Analysis and further improvement of fine resolution frequency estimation method from three DFT samples”, IEEE Signal Processing Letters, 20(9), 913-916, (2013).
• [29] Ortatatlı, İ.E, et al. Real-time frequency parameter extraction for electronic support systems, “IEEE 24th Signal Processing and Communication Application Conference (SIU)”, 105-108, (2016).
• [30] Jacobsen, E., Kootsookos, P. ”Fast, accurate frequency estimators”, DSP Tips & Tricks IEEE Signal Processing Magazine, 24:3-123-125, (2007).
• [31] Eroğlu, M., Narin, Ö. G, “İnsansız hava aracı ile üretilen Sayısal Yükseklik Modeli (SYM) ile Google Earth ve HGM Küre verilerinin karşılaştırılması”, Politeknik Dergisi, 24(2), 545-551, (2021).
• [32] Koç, K., Demirtaş, M., Çetinbaş, I. “Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer”, Politeknik Dergisi, 1-1, (2022).